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<li><a class="reference internal" href="#">News</a><ul>
<li><a class="reference internal" href="#hiddenbiggerheadingfont"><span class="hidden">HiddenBiggerHeadingFont</span></a><ul>
<li><a class="reference internal" href="#nov-03-2025-networkit-11-2-released">Nov 03, 2025: <strong>NetworKit 11.2 released</strong></a></li>
<li><a class="reference internal" href="#mar-10-2025-networkit-11-1-released">Mar 10, 2025: <strong>NetworKit 11.1 released</strong></a></li>
<li><a class="reference internal" href="#mar-15-2024-networkit-day-2024">Mar 15, 2024: <strong>NetworKit Day 2024</strong></a></li>
<li><a class="reference internal" href="#feb-06-2024-save-the-date-networkit-day-2024">Feb 06, 2024: <strong>Save the Date: NetworKit Day 2024</strong></a></li>
<li><a class="reference internal" href="#jan-30-2024-networkit-11-0-released">Jan 30, 2024: <strong>NetworKit 11.0 released</strong></a></li>
<li><a class="reference internal" href="#mar-23-2023-networkit-10-1-released">Mar 23, 2023: <strong>NetworKit 10.1 released</strong></a></li>
<li><a class="reference internal" href="#may-18-2022-networkit-10-0-released">May 18, 2022: <strong>NetworKit 10.0 released</strong></a></li>
<li><a class="reference internal" href="#march-14-2022-new-paper-using-networkit">March 14, 2022: <strong>New paper using NetworKit</strong></a></li>
<li><a class="reference internal" href="#february-8-2022-networkit-day-2022-march-3rd">February 8, 2022: <strong>NetworKit Day 2022 - March 3rd</strong></a></li>
<li><a class="reference internal" href="#december-20-2021-save-the-date-networkit-day-2022">December 20, 2021: <strong>Save the Date: NetworKit Day 2022</strong></a></li>
<li><a class="reference internal" href="#december-14-2021-networkit-9-1-1-released">December 14, 2021: <strong>NetworKit 9.1.1 released</strong></a></li>
<li><a class="reference internal" href="#july-1-2021-networkit-9-0-released">July 1, 2021: <strong>NetworKit 9.0 released</strong></a></li>
<li><a class="reference internal" href="#february-23-2021-networkit-8-1-released">February 23, 2021: <strong>NetworKit 8.1 released</strong></a></li>
<li><a class="reference internal" href="#january-15-2021-new-paper-using-networkit">January 15, 2021: <strong>New paper using NetworKit</strong></a></li>
<li><a class="reference internal" href="#december-18-2020-networkit-8-0-released">December 18, 2020: <strong>NetworKit 8.0 released</strong></a></li>
<li><a class="reference internal" href="#september-08-2020-networkit-7-1-released">September 08, 2020: <strong>NetworKit 7.1 released</strong></a></li>
<li><a class="reference internal" href="#may-29-2020-networkit-7-0-released">May 29, 2020: <strong>NetworKit 7.0 released</strong></a></li>
<li><a class="reference internal" href="#march-2020-new-accepted-papers-using-networkit">March 2020: <strong>new accepted papers using NetworKit</strong></a></li>
<li><a class="reference internal" href="#march-1-2020-networkit-6-1-0-released">March 1, 2020: <strong>NetworKit 6.1.0 released</strong></a></li>
<li><a class="reference internal" href="#february-24-2020-networkit-6-0-1-released">February 24, 2020: <strong>NetworKit 6.0.1 released</strong></a></li>
<li><a class="reference internal" href="#november-29-2019-networkit-6-0-released">November 29, 2019: <strong>NetworKit 6.0 released</strong></a></li>
<li><a class="reference internal" href="#november-2019-new-accepted-papers-using-networkit">November 2019: new accepted papers using NetworKit</a></li>
<li><a class="reference internal" href="#december-19-2018-networkit-5-0-released">December 19, 2018: <strong>NetworKit 5.0 released</strong></a></li>
<li><a class="reference internal" href="#june-25-2018-networkit-4-6-released">June 25, 2018: <strong>NetworKit 4.6 released</strong></a></li>
<li><a class="reference internal" href="#december-14-2017-networkit-4-5-released">December 14, 2017: <strong>NetworKit 4.5 released</strong></a></li>
<li><a class="reference internal" href="#september-06-2017-networkit-4-4-released">September 06, 2017: <strong>NetworKit 4.4 released</strong></a></li>
<li><a class="reference internal" href="#july-19-2017-networkit-day-on-september-12-2017">July 19, 2017: <strong>NetworKit Day</strong> on September 12, 2017</a></li>
<li><a class="reference internal" href="#june-07-2017-networkit-4-3-released">June 07, 2017: <strong>NetworKit 4.3 released</strong></a></li>
<li><a class="reference internal" href="#march-29-2017-publication-accepted-at-sea-2017">March 29, 2017: <strong>Publication accepted at SEA 2017</strong></a></li>
<li><a class="reference internal" href="#february-25-2017-migration-to-github">February 25, 2017: <strong>Migration to GitHub</strong></a></li>
<li><a class="reference internal" href="#december-13-2016-networkit-4-2-released">December 13, 2016: <strong>NetworKit 4.2 released</strong></a></li>
<li><a class="reference internal" href="#july-06-2016-publication-accepted-at-csc-2016">July 06, 2016: <strong>Publication accepted at CSC 2016</strong></a></li>
<li><a class="reference internal" href="#july-05-2016-networkit-4-1-1-released">July 05, 2016: <strong>NetworKit 4.1.1 released</strong></a></li>
<li><a class="reference internal" href="#june-23-2016-networkit-4-1-released">June 23, 2016: <strong>NetworKit 4.1 released</strong></a></li>
<li><a class="reference internal" href="#may-9-2016-networkit-journal-paper-accepted-at-network-science">May 9, 2016: <strong>NetworKit journal paper accepted at Network Science</strong></a></li>
<li><a class="reference internal" href="#apr-12-2016-publication-accepted-at-snam">Apr 12, 2016: <strong>Publication accepted at SNAM</strong></a></li>
<li><a class="reference internal" href="#apr-12-2016-publication-accepted-at-internet-mathematics">Apr 12, 2016: <strong>Publication accepted at Internet Mathematics</strong></a></li>
<li><a class="reference internal" href="#nov-16-2016-publication-accepted-at-alenex16">Nov 16, 2016: <strong>Publication accepted at ALENEX16</strong></a></li>
<li><a class="reference internal" href="#nov-10-2015-networkit-4-0-released">Nov 10, 2015: <strong>NetworKit 4.0 released</strong></a></li>
<li><a class="reference internal" href="#aug-19-2015-networkit-3-6-released">Aug 19, 2015: <strong>NetworKit 3.6 released</strong></a></li>
<li><a class="reference internal" href="#jun-16-2015-publication-accepted-at-esa15">Jun 16, 2015: <strong>Publication accepted at ESA15</strong></a></li>
<li><a class="reference internal" href="#jun-9-2015-networkit-3-5-released">Jun 9, 2015: <strong>NetworKit 3.5 released</strong></a></li>
<li><a class="reference internal" href="#dec-4-2014-networkit-3-4-released">Dec 4, 2014: <strong>NetworKit 3.4 released</strong></a></li>
<li><a class="reference internal" href="#oct-21-2014-publication-accepted-at-alenex15">Oct 21, 2014: <strong>Publication accepted at ALENEX15</strong></a></li>
<li><a class="reference internal" href="#sep-28-2014-networkit-presented-at-summer-school-tutorial-on-network-analysis">Sep 28, 2014: <strong>NetworKit presented at summer school tutorial on network analysis</strong></a></li>
<li><a class="reference internal" href="#sep-28-2014-publication-accepted">Sep 28, 2014: <strong>Publication accepted</strong></a></li>
<li><a class="reference internal" href="#aug-22-2014-networkit-3-3-released">Aug 22, 2014: <strong>NetworKit 3.3 released</strong></a></li>
<li><a class="reference internal" href="#jul-1-2014-networkit-3-2-released">Jul 1, 2014: <strong>NetworKit 3.2 released</strong></a></li>
<li><a class="reference internal" href="#may-15-2014-new-website-online">May 15, 2014: <strong>New website online</strong></a></li>
<li><a class="reference internal" href="#apr-25-2014-introductory-talk">Apr 25, 2014: <strong>Introductory talk</strong></a></li>
<li><a class="reference internal" href="#apr-15-2014-networkit-3-1-released">Apr 15, 2014: <strong>NetworKit 3.1 released</strong></a></li>
<li><a class="reference internal" href="#mar-13-2014-networkit-3-0-released">Mar 13, 2014: <strong>NetworKit 3.0 released</strong></a></li>
<li><a class="reference internal" href="#nov-11-2013-networkit-2-0-released">Nov 11, 2013: <strong>NetworKit 2.0 released</strong></a></li>
<li><a class="reference internal" href="#mar-17-2013-networkit-1-0-released">Mar 17, 2013: <strong>NetworKit 1.0 released</strong></a></li>
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<section id="news">
<h1>News<a class="headerlink" href="#news" title="Link to this heading">¶</a></h1>
<section id="hiddenbiggerheadingfont">
<h2><span class="hidden">HiddenBiggerHeadingFont</span><a class="headerlink" href="#hiddenbiggerheadingfont" title="Link to this heading">¶</a></h2>
<section id="nov-03-2025-networkit-11-2-released">
<h3>Nov 03, 2025: <strong>NetworKit 11.2 released</strong><a class="headerlink" href="#nov-03-2025-networkit-11-2-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New Features</span>:</dt><dd><ul class="simple">
<li><p>Add Python bindings for Kruskal and Prim Minimum Spanning Forest algorithms in the <code class="code docutils literal notranslate"><span class="pre">graph</span></code> module. Use <code class="code docutils literal notranslate"><span class="pre">nk.graph.KruskalMSF</span></code> and <code class="code docutils literal notranslate"><span class="pre">nk.graph.PrimMSF</span></code> to create an instance of the respective algorithm.</p></li>
</ul>
</dd>
<dt><span class="underline">New algorithms</span>:</dt><dd><ul class="simple">
<li><p>Successive Shortest Path (SSP) algorithm for solving the Minimum-Cost Flow problem. The algorithm is currently only available in C++ via <code class="code docutils literal notranslate"><span class="pre">NetworKit::SuccessiveShortestPathMinCostFlow</span></code>. Thanks to Andreas Scharf (@Schwarf) for the contribution.</p></li>
<li><p>Dinic’s blocking-flow algorithm for calculating maximum flow in directed, weighted graphs. The algorithm is currently only available in C++ via <code class="code docutils literal notranslate"><span class="pre">NetworKit::Dinic</span></code>. Thanks to Andreas Scharf (@Schwarf) for the contribution.</p></li>
<li><p>Floyd-Warshall algorithm for computing all-pairs shortest paths in a weighted graph. The algorithm is currently only available in C++ via <code class="code docutils literal notranslate"><span class="pre">NetworKit::FloydWarshall</span></code>. Thanks to Andreas Scharf (@Schwarf) for the contribution.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span>:</dt><dd><ul class="simple">
<li><p>Introduce <code class="code docutils literal notranslate"><span class="pre">getIndicator()</span></code> in <code class="code docutils literal notranslate"><span class="pre">RandomMaximumSpanningForest</span></code> and <code class="code docutils literal notranslate"><span class="pre">UnionMaximumSpanningForest</span></code>, which is an alias for <code class="code docutils literal notranslate"><span class="pre">getAttribute</span></code>. <code class="code docutils literal notranslate"><span class="pre">getAttribute</span></code> is deprecated in order to avoid confusion with edge / node attributes in the graph datastructure.</p></li>
<li><p>Refactor <code class="code docutils literal notranslate"><span class="pre">addEdges()</span></code> in the Python interface of the <code class="code docutils literal notranslate"><span class="pre">Graph</span></code> class, such that unnecessary copies of the input edge list are avoided. As a result, there is a potential speedup when adding batches of edges to the graph. This also affects the constructor <code class="code docutils literal notranslate"><span class="pre">GraphFromCoo</span></code>. See <a class="reference external" href="https://github.com/networkit/networkit/pull/1369">#1369</a> for details.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span>:</dt><dd><ul class="simple">
<li><p>Fix a bug in APSP algorithm, where larger matrices create an overflow of internal 32-bit helper variables when using <code class="code docutils literal notranslate"><span class="pre">getDistances()</span></code>. See <a class="reference external" href="https://github.com/networkit/networkit/issues/1366">#1366</a> for details.</p></li>
<li><p>Fix a bug when writing empty graphs to NetworKitBinary files. See <a class="reference external" href="https://github.com/networkit/networkit/pull/1338">#1338</a> for details.</p></li>
<li><p>Fix bug in <code class="code docutils literal notranslate"><span class="pre">ParallelLeiden</span></code>, where the algorithm could create disconnected communities. See <a class="reference external" href="https://github.com/networkit/networkit/pull/1328">#1328</a> for details.</p></li>
</ul>
</dd>
</dl>
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<section id="mar-10-2025-networkit-11-1-released">
<h3>Mar 10, 2025: <strong>NetworKit 11.1 released</strong><a class="headerlink" href="#mar-10-2025-networkit-11-1-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New features</span>:</dt><dd><ul class="simple">
<li><p>New RB-Matrix and Graph reader. It is available via <code class="code docutils literal notranslate"><span class="pre">nk.graphio.RBGraphReader</span></code> or <code class="code docutils literal notranslate"><span class="pre">nk.readGraph(path,format=nk.graphio.Format.RB)</span></code> (<a class="reference external" href="https://github.com/networkit/networkit/pull/1264">#1264</a>).</p></li>
<li><p>The MatrixMarket reader is now available in C++ and Python (<a class="reference external" href="https://github.com/networkit/networkit/pull/1236">#1236</a>, <a class="reference external" href="https://github.com/networkit/networkit/pull/1262/">#1262</a>)</p></li>
<li><p>Add new function <code class="code docutils literal notranslate"><span class="pre">isBipartite</span></code>, which checks whether a given undirected graph is bipartite. Currently only implemented in C++ (<a class="reference external" href="https://github.com/networkit/networkit/pull/1304">#1304</a>).</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">nk.graphtools.topologicalSort</span></code> now has an additional parameter <code class="code docutils literal notranslate"><span class="pre">nodeIdMap</span></code>, supplying a map of node ids, if the nodes in the graph are non-contiuous (<a class="reference external" href="https://github.com/networkit/networkit/pull/1241/">#1241</a>).</p></li>
<li><p>Refactored the existing implementation of the Barabasi-Albert graph generator, leading to a significant speedup (<a class="reference external" href="https://github.com/networkit/networkit/pull/1175">#1175</a>) for multicore systems due to parallelization. The new implementation is based on “Scalable generation of scale-free graphs” by Sanders and Schulz.</p></li>
<li><p>A similar treatment was done for RMat-Generator, the Configuration Model and the Chung-Lu Model (<a class="reference external" href="https://github.com/networkit/networkit/pull/1164">#1164</a>, <a class="reference external" href="https://github.com/networkit/networkit/pull/1186">#1186</a>, <a class="reference external" href="https://github.com/networkit/networkit/pull/1177">#1177</a>)</p></li>
<li><p>LAMG-solver now supports multiple connected components in parallel (<a class="reference external" href="https://github.com/networkit/networkit/pull/1212">#1212</a>).</p></li>
<li><p>The <code class="code docutils literal notranslate"><span class="pre">vizbridges</span></code> module now also supports the visualization of edge attributes (<a class="reference external" href="https://github.com/networkit/networkit/pull/1230">#1230</a>).</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">DynBetweennessOneNode</span></code> (in module <code class="code docutils literal notranslate"><span class="pre">centrality</span></code>) now also supports weighted graphs (<a class="reference external" href="https://github.com/networkit/networkit/pull/1245">#1245</a>).</p></li>
</ul>
</dd>
<dt><span class="underline">New algorithms</span>:</dt><dd><ul class="simple">
<li><p>Add <a class="reference external" href="https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7963e9feffe1c9362eb1a69010a5139d1da3661e">Left-Right Planarity Test</a> algorithm by U. Brandes, which is used to determine whether a given graph is planar. This algorithm is available in C++ in the new <code class="code docutils literal notranslate"><span class="pre">planarity</span></code> module. Thanks for <a class="reference external" href="https://github.com/Schwarf">@Schwarf</a> for the contribution.</p></li>
<li><p>Add b-Suitor algorithm both in a static version based on <a class="reference external" href="https://www.osti.gov/servlets/purl/1418504">Efficient Approx. Algorithms for weighted b-Matching</a> by Khan et al. and a edge dynamic version based on <a class="reference external" href="https://arxiv.org/abs/2410.09965">A Fully-dynamic Approximation Algorithm for Maximum Weight b-Matchings in Graphs</a> by Brandt-Tumescheit et al. Both algorithms are available in C++ and Python in the <code class="code docutils literal notranslate"><span class="pre">matching</span></code> module.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span>:</dt><dd><ul class="simple">
<li><p>Python 3.13 and Numpy 2.X is fully supported, Python versions <code class="code docutils literal notranslate"><span class="pre"><3.10</span></code> is not supported anymore. This means, that wheels will only be available for 3.10 or higher.</p></li>
<li><p>The oldest supported compiler are now GCC 10, LLVM 10. This enables the usage of C++20 features in the code base.</p></li>
<li><p>Refactor CI pipeline (github actions) in order to detect memory leaks. As a result several minor possible leaks have been resolved (<a class="reference external" href="https://github.com/networkit/networkit/pull/1283">#1283</a>).</p></li>
<li><p>As shown by Sahu et. al in “GVE-Leiden: Fast Leiden Algorithm for Community Detection in Shared Memory Setting” the current implementation of <code class="code docutils literal notranslate"><span class="pre">ParallelLeiden</span></code> might create a small fraction of disconnected communities. This behaviour is now documented and will be fixed in a future update.</p></li>
<li><p>Building NetworKit via <code class="code docutils literal notranslate"><span class="pre">setup.py</span></code> does not anymore detect compatible compilers. This is now done in CMake directly, making the building process more robust.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span>:</dt><dd><ul class="simple">
<li><p>Fix attribute assignment from another attribute <code class="code docutils literal notranslate"><span class="pre">attr[i]</span> <span class="pre">=</span> <span class="pre">attr[j]</span></code> and general copy instructions (<a class="reference external" href="https://github.com/networkit/networkit/pull/1279">#1279</a>, <a class="reference external" href="https://github.com/networkit/networkit/pull/1251">#1251</a>).</p></li>
<li><p>Fix edge attributes indexing on dynamic graphs (edge removal, insertion) (<a class="reference external" href="https://github.com/networkit/networkit/pull/1257">#1257</a>, <a class="reference external" href="https://github.com/networkit/networkit/pull/1257">#1299</a>).</p></li>
<li><p>Fixed a bug in <code class="code docutils literal notranslate"><span class="pre">SignalHandler</span></code>, which led to inconsistent behavior when interrupting parallel code (<a class="reference external" href="https://github.com/networkit/networkit/pull/1301">#1301</a>).</p></li>
<li><p>Fix <code class="code docutils literal notranslate"><span class="pre">RMatGenerator</span></code> for instances where the number of nodes exceeded the limit of <code class="code docutils literal notranslate"><span class="pre">uint32_t</span></code> (<a class="reference external" href="https://github.com/networkit/networkit/pull/1268">#1268</a>).</p></li>
<li><p>Fix the product of two <code class="code docutils literal notranslate"><span class="pre">DynamicMatrix</span></code> instances (<a class="reference external" href="https://github.com/networkit/networkit/pull/1252">#1252</a>).</p></li>
<li><p>Fix a problem in <code class="code docutils literal notranslate"><span class="pre">DynDijkstra</span></code>, whereby the shortest path itself was not created properly (<a class="reference external" href="https://github.com/networkit/networkit/pull/1240">#1240</a>).</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">addEdge</span></code> now correctly adds missing nodes on dynamic graphs with id holes (<a class="reference external" href="https://github.com/networkit/networkit/pull/1232">#1232</a>).</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">networkit.community.OverlappingNMIDistance</span></code> is working again (<a class="reference external" href="https://github.com/networkit/networkit/pull/1229">#1229</a>).</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">networkit.generators.PowerlawDegreeSequence</span></code> is working again (<a class="reference external" href="https://github.com/networkit/networkit/pull/1196">#1196</a>).</p></li>
</ul>
</dd>
</dl>
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<section id="mar-15-2024-networkit-day-2024">
<h3>Mar 15, 2024: <strong>NetworKit Day 2024</strong><a class="headerlink" href="#mar-15-2024-networkit-day-2024" title="Link to this heading">¶</a></h3>
<p>As already announced - we are looking forward to a new NetworKit Day in 2024,
taking place on April 9th from 2 p.m. to 6 p.m. (CEST) online via Zoom.
Registration is mandatory (to receive the Zoom link), but free of charge.</p>
<p>This event is - like the previous ones - about interacting with the community.
We share our latest updates, provide insights for new users and also offer two
short tutorials: one for beginners and one for advanced users. We also intend
to discuss future development directions and receive feedback on the current
status of NetworKit.</p>
<p>We are also pleased to feature two invited guest talks by Jonathan Donges
(Potsdam Institute for Climate Impact Research) and Kathrin Hanauer
(University of Vienna).</p>
<p>The program of the event can be found on the event subpage:
<a class="reference external" href="https://networkit.github.io/networkit-day.html">https://networkit.github.io/networkit-day.html</a></p>
<p>Link for registration:
<a class="reference external" href="https://www.eventbrite.de/e/networkit-day-2024-nd24-tickets-825016084317">https://www.eventbrite.de/e/networkit-day-2024-nd24-tickets-825016084317</a></p>
<p>Looking forward to seeing you on April 9th!</p>
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</section>
<section id="feb-06-2024-save-the-date-networkit-day-2024">
<h3>Feb 06, 2024: <strong>Save the Date: NetworKit Day 2024</strong><a class="headerlink" href="#feb-06-2024-save-the-date-networkit-day-2024" title="Link to this heading">¶</a></h3>
<p>We are happy to announce a new NetworKit Day. The event will take place on April, 9th 2024 - starting at 1 p.m. and ending at 6 p.m CET. Details
concerning the program schedule will be shared at a later date.</p>
<p>Best Wishes!</p>
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</section>
<section id="jan-30-2024-networkit-11-0-released">
<h3>Jan 30, 2024: <strong>NetworKit 11.0 released</strong><a class="headerlink" href="#jan-30-2024-networkit-11-0-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New features</span>:</dt><dd><ul class="simple">
<li><p>The <code class="code docutils literal notranslate"><span class="pre">graphio</span></code> module now supports guessing the file format of a given graph. <code class="code docutils literal notranslate"><span class="pre">nk.graphio.guessFileFormat</span></code> either returns a supported
graph format from the list (see: <a class="reference external" href="https://networkit.github.io/dev-docs/python_api/graphio.html#networkit.graphio.Format">https://networkit.github.io/dev-docs/python_api/graphio.html#networkit.graphio.Format</a>) or an <code class="code docutils literal notranslate"><span class="pre">IOError</span></code>.
This functionality is used now, if a graph is read by <code class="code docutils literal notranslate"><span class="pre">graphio.readGraph</span></code> without giving the file format. See the corresponding
IO-notebook for more information about the usage.</p></li>
<li><p>The graph datastructure now supports <code class="code docutils literal notranslate"><span class="pre">EdgeAttributes</span></code>. These can be attached/removed/edited similarly to node attributes via Python and
C++.</p></li>
<li><p>Refactored <code class="code docutils literal notranslate"><span class="pre">Matrices</span></code> from the <code class="code docutils literal notranslate"><span class="pre">algebraic</span></code> module. This includes rewriting certain functions and adding new functionality. For
<code class="code docutils literal notranslate"><span class="pre">CSRMatrix</span></code>: functions for iterating over all elements of the matrix, including zeros. For <code class="code docutils literal notranslate"><span class="pre">DenseMatrix</span></code>: functions to compute the
adjacency, Laplacian, and incidence matrix of a graph and a diagonal matrix. For <code class="code docutils literal notranslate"><span class="pre">DynamcMatrix</span></code>: functions to iterate over the (non-zero)
elements. In this way, all matrix classes offer the same set of APIs.</p></li>
<li><p>A graph can now be constructed from several numpy/scipy data types, including <code class="code docutils literal notranslate"><span class="pre">scipy.sparse.coo_matrix</span></code>. The new function is available via
<code class="code docutils literal notranslate"><span class="pre">nk.GraphFromCoo</span></code>. See the corresponding graph-notebook for more information about the usage.</p></li>
<li><p>The new constructor uses <code class="code docutils literal notranslate"><span class="pre">graph.addEdges</span></code> underneath, which is also now available via the Python interface. Based on several numpy/scipy
data sources, multiple edges can be added. This method has increased performance (5x-10x) with respect to manually creating the graph and adding edges in
a loop.</p></li>
<li><p>The <code class="code docutils literal notranslate"><span class="pre">plot</span></code> module in Python was completely rewritten, since most of its functionality was outdated. In addition a notebook is added,
reflecting the changes.</p></li>
</ul>
</dd>
<dt><span class="underline">New algorithms (distance)</span>:</dt><dd><ul class="simple">
<li><p>Dynamic 2-Hop Landmark Labeling based on <a class="reference external" href="https://dl.acm.org/doi/10.1145/3299901">https://dl.acm.org/doi/10.1145/3299901</a> . The current implementation is only partially dynamic, since
only edge insertions are supported.</p></li>
<li><p>Complex Paths algorithm based on <a class="reference external" href="https://www.nature.com/articles/s41467-021-24704-6">https://www.nature.com/articles/s41467-021-24704-6</a> and the original implementation from
<a class="reference external" href="https://github.com/drguilbe/complexpaths.git">https://github.com/drguilbe/complexpaths.git</a> . The algorithm is part of the <code class="code docutils literal notranslate"><span class="pre">centrality</span></code> module and available in C++ and Python.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span>:</dt><dd><ul class="simple">
<li><p>Python 3.12 is now fully supported.</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">compactEdges()</span></code> is now marked as deprecated, since there can not be any holes for missing edges in the data structure anymore. The
underlying idea of keeping a consistent datastructure (maintaining ordering of edges / correct indexes of edge ids) after dynamic graph updates
has been moved to new functions <code class="code docutils literal notranslate"><span class="pre">setKeepEdgesSorted()</span></code> and <code class="code docutils literal notranslate"><span class="pre">setMaintainCompactEdges()</span></code>. These functions set variables, which
influence running time consuming sanity checks during <code class="code docutils literal notranslate"><span class="pre">removeEdge</span></code>. Per default the variables are set to <code class="code docutils literal notranslate"><span class="pre">false</span></code> and it is only
needed to activate them for advanced use cases.</p></li>
<li><p>Several deprecated functions are removed entirely or are now integrated in different modules with release 11.0. Some smaller or outdated helper
modules like <code class="code docutils literal notranslate"><span class="pre">stopwatch</span></code> are also removed without replacements. This concerns mostly the Python interface.</p></li>
<li><p>Supported compiler now include GCC 13 and Clang 17. Note that compiler older than five years are not officially supported anymore (no changes
with respect to release 10.1 of NetworKit).</p></li>
<li><p>The <code class="code docutils literal notranslate"><span class="pre">NetworKitBinary</span></code> writer now converts older binary formats (<code class="code docutils literal notranslate"><span class="pre"><3</span></code>) automatically to the most recent version 3. With respect to
previous versions, version 3 supports edge ids.</p></li>
<li><p>The running time of <code class="code docutils literal notranslate"><span class="pre">HyperbolicGenerator</span></code> is improved slightly by using an algorithm, which generates sequences of sorted random numbers.</p></li>
<li><p>Functions <code class="code docutils literal notranslate"><span class="pre">RandomEdgeScore::score</span></code>, <code class="code docutils literal notranslate"><span class="pre">RandomNodeEdgeScore::score</span></code> and <code class="code docutils literal notranslate"><span class="pre">Sfigality::maximum</span></code> are now implemented.</p></li>
<li><p>A new notebook is added, describing the usage of dynamic algorithms in NetworKit. It is available via github and the documentation page.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span>:</dt><dd><ul class="simple">
<li><p>Fixed a bug in <code class="code docutils literal notranslate"><span class="pre">DGSWriter</span></code>, where node restoration events were not correctly written to files.</p></li>
<li><p>Fixed functions <code class="code docutils literal notranslate"><span class="pre">communicationGraph</span></code> and <code class="code docutils literal notranslate"><span class="pre">weightedDegreeWithCluster</span></code> from <code class="code docutils literal notranslate"><span class="pre">GraphClusteringTools</span></code> to return correct data types
(directed/weighted).</p></li>
<li><p>Subtraction of two <code class="code docutils literal notranslate"><span class="pre">DenseMatrix</span></code> now works correctly.</p></li>
<li><p>Fixed the previously broken <code class="code docutils literal notranslate"><span class="pre">community.SpectralPartitioner</span></code>.</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">community.kCoreCommunityDetection</span></code> gives now correct results.</p></li>
<li><p>Fixed a heap corruption bug due to missing parameters in the Python interface of <code class="code docutils literal notranslate"><span class="pre">KadabraBetweenness</span></code>.</p></li>
<li><p>Fixed a bug in <code class="code docutils literal notranslate"><span class="pre">BiconnectedComponents</span></code>, where the result of the algorithm was incorrect after a node got deleted from the graph data
structure.</p></li>
<li><p>Removed a race condition from <code class="code docutils literal notranslate"><span class="pre">Luby</span></code> algorithm, leading to erroneous results. As a result, the performance of the algorithm has decreased
(2-3x loss of speedup).</p></li>
</ul>
</dd>
</dl>
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<section id="mar-23-2023-networkit-10-1-released">
<h3>Mar 23, 2023: <strong>NetworKit 10.1 released</strong><a class="headerlink" href="#mar-23-2023-networkit-10-1-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New features</span>:</dt><dd><ul class="simple">
<li><p><code class="code docutils literal notranslate"><span class="pre">TopCloseness</span></code> and <code class="code docutils literal notranslate"><span class="pre">TopHarmonicCloseness</span></code> now support restriction of the top-k calculation to certain nodes (while the truth is
given by the complete graph). This can lead to significant speed-up in running time.</p></li>
<li><p>It is now possible to let <code class="code docutils literal notranslate"><span class="pre">Graph.addEdge()</span></code> check for multi-edges when adding new edges. This is disabled by default, since it has an
impact on the running time of <code class="code docutils literal notranslate"><span class="pre">addEdge()</span></code>. The return type changed to indicate whether the edge was added or not.</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">Node2Vec</span></code> now also supports directed graphs. This was a contribution from Klaus Ahrens (@fidus58).</p></li>
<li><p>Edge weights in a graph can now be randomized by calling <code class="code docutils literal notranslate"><span class="pre">GraphTools::randomizeWeights()</span></code> (C++) or
<code class="code docutils literal notranslate"><span class="pre">networkit.graphtools.randomizeWeights()</span></code>. The C++ API also supports adding a custom distribution.</p></li>
</ul>
</dd>
<dt><span class="underline">New algorithms (distance)</span>:</dt><dd><ul class="simple">
<li><p>New algorithm: Pruned Landmark Labeling based on T. Akiba, Y. Iwata, Y. Yoshida, SIGMOD ‘13. The algorithm computes distance labels which are
used to answer shortest-path distance queries.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span>:</dt><dd><ul class="simple">
<li><p>Python 3.11 is now fully supported. With release 10.1 onward, a wheel for Linux, macOS and Windows will be available via all distribution
channels.</p></li>
<li><p>Supported compiler now include GCC 12.0 and Clang 15.0. Note that compiler older than five years are not officially supported anymore. This now
includes Clang <code class="code docutils literal notranslate"><span class="pre"><6.0</span></code> and GCC <code class="code docutils literal notranslate"><span class="pre"><8.1</span></code> (with the exception of <code class="code docutils literal notranslate"><span class="pre">7.4</span></code>).</p></li>
<li><p>Calling names for enums in both Python and C++ is now unified. Before the change, different enums were written with different naming schemes
(for example: ClosenessVariant::standard, ClosenessType::OUTBOUND). Also naming scheme between Python and C++ differed in various cases. The new
convention is: <code class="code docutils literal notranslate"><span class="pre">CamelCase</span></code> for identifiers and <code class="code docutils literal notranslate"><span class="pre">SCREAMING_SNAKE_CASE</span></code> for members. For backwards compatibility all previous calling
conventions still work (for two releases).</p></li>
<li><p>In addition all enums in Python are now callable as member of their module. For example: <code class="code docutils literal notranslate"><span class="pre">networkit.centrality.ClosenessType.OUTBOUND</span></code>.</p></li>
<li><p>Previously non-existing edge ids were returned as 0 which could be misleading. Now they return as <code class="code docutils literal notranslate"><span class="pre">none</span></code> to be clear that the edge id
doesn’t exist. See <a class="reference external" href="https://github.com/networkit/networkit/issues/747">https://github.com/networkit/networkit/issues/747</a> for details.</p></li>
<li><p>For <code class="code docutils literal notranslate"><span class="pre">SpanningEdgeCentrality</span></code>, it is now mandatory to index the edges before running the algorithm. See
<a class="reference external" href="https://github.com/networkit/networkit/issues/967">https://github.com/networkit/networkit/issues/967</a> for details.</p></li>
<li><p>Improved <code class="code docutils literal notranslate"><span class="pre">MatrixMarketReader</span></code> now supports <code class="code docutils literal notranslate"><span class="pre">%</span></code>-comments and warns for potential data loss for edge weights bigger than
<code class="code docutils literal notranslate"><span class="pre">4.5*10^15</span></code>.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span>:</dt><dd><ul class="simple">
<li><p>Fixed bug in <code class="code docutils literal notranslate"><span class="pre">ParallelConnectedComponents</span></code>, which lead to occasional segmentation faults in the member function <cite>getComponents()</cite>.</p></li>
<li><p><code class="code docutils literal notranslate"><span class="pre">Graph</span></code> constructor now supports creation of graphs with indexed edges by passing <code class="code docutils literal notranslate"><span class="pre">edgesIndexed=True</span></code>. Before the fix doing so led
to segmentation faults.</p></li>
<li><p>Fixed bug for source-target shortest path algorithms (<code class="code docutils literal notranslate"><span class="pre">MultiTargetBFS</span></code>, <code class="code docutils literal notranslate"><span class="pre">MultiTargetDijkstra</span></code>), which caused segmentation faults
when passing unreachable targets.</p></li>
<li><p>Fixed inconsistent weights for graphs created by <code class="code docutils literal notranslate"><span class="pre">GraphTools::toUndirected()</span></code>/<code class="code docutils literal notranslate"><span class="pre">graphtools.toUndirected()</span></code>. Error occured when
converting bidirectional edges. Fixed behavior per default creates an undirected edge with the summed up weight of both edges.</p></li>
<li><p>Fix a potential bug in PLP. A variable was updated non-atomically in a parallel loop, which can lead to a possible race condition.</p></li>
<li><p>Fixed <code class="code docutils literal notranslate"><span class="pre">NetworkBinaryWriter</span></code> error, which led to erroneous graph files when writing graphs with deleted nodes (e.g. by calling
<code class="code docutils literal notranslate"><span class="pre">G::removeNode(u)</span></code>).</p></li>
<li><p>Fix EdmondsKarp <code class="code docutils literal notranslate"><span class="pre">getMaxFlow()</span></code> (for directed graphs) and <code class="code docutils literal notranslate"><span class="pre">getSourceSet()</span></code> (for directed/undirected graphs). This is a contribution
from Jonas Charfreitag (@CharJon).</p></li>
</ul>
</dd>
</dl>
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<section id="may-18-2022-networkit-10-0-released">
<h3>May 18, 2022: <strong>NetworKit 10.0 released</strong><a class="headerlink" href="#may-18-2022-networkit-10-0-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New features</span>:</dt><dd><ul class="simple">
<li><p>Native support for node attributes. In C++ the attributes can be of any type. Python does not support generic data types; thus, NetworKit node
attributes in Python are restriced to type <code class="code docutils literal notranslate"><span class="pre">int</span></code>, <code class="code docutils literal notranslate"><span class="pre">float</span></code>, and <code class="code docutils literal notranslate"><span class="pre">str</span></code>, and may be subject to changes in the future. See
<a class="reference external" href="https://networkit.github.io/dev-docs/python_api/graph.html#networkit.graph.Graph.attachNodeAttribute">https://networkit.github.io/dev-docs/python_api/graph.html#networkit.graph.Graph.attachNodeAttribute</a> for details. The attribute API is still
considered experimental and may change in the future.</p></li>
<li><p>New Python module <code class="code docutils literal notranslate"><span class="pre">vizbridges</span></code>: provides functions for 2D and 3D graph visualization (via Cytoscape/Plotly) within Jupyter Notebooks. See
the documentation and our example notebooks for more details. Module <code class="code docutils literal notranslate"><span class="pre">csbridge</span></code> is deprecated in favor of vizbridges and respective
functionality is moved there. An application built on top of vizbridges is described in “Interactive Visualization of Protein RINs using
NetworKit in the Cloud” (E. Angriman, F. Brandt-Tumescheit, L. Franke, A. van der Grinten, H. Meyerhenke).</p></li>
</ul>
</dd>
<dt><span class="underline">New algorithms (centrality)</span>:</dt><dd><ul class="simple">
<li><p>New algorithm for computing the Local Clustering Coefficient based on squares. This is a contribution from Till Hoffmann (@tillahoffmann) from
Harvard T.H. Chan School of Public Health.</p></li>
<li><p>New algorithm for Forest Closeness Centrality based on “New Approximation Algorithms for Forest Closeness Centrality - for Individual Vertices
and Vertex Groups”, A. van der Grinten, E. Angriman, M. Predari, H. Meyerhenke, SDM21.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span>:</dt><dd><ul class="simple">
<li><p>C++ standard updated to version 17, oldest supported compilers are Clang 5.0, GCC 7 (and equivalent MSVC, AppleClang).</p></li>
<li><p>APSP: support for graphs with non-existing nodes.</p></li>
<li><p>SPSP: support for a list of target nodes; the algorithm stops once all target nodes have been visited.</p></li>
<li><p>Distance module: all algorithms support returning distances as a numpy array (via <code class="code docutils literal notranslate"><span class="pre">getDistances()</span></code>), which is more efficient than
returning Python lists. The new approach also enables straightforward consumption of centrality scores by numpy-compatible APIs and may be
enabled by default in the future. This is a contribution from Till Hoffmann (@tillahoffmann) from Harvard T.H. Chan School of Public Health.</p></li>
<li><p>Dynamics module: possibility to compare graph events via binary operators, available both in C++ and Python.</p></li>
<li><p>Generators module: removal of the quadratic version of the Barabasi Albert Generator. See <a class="reference external" href="https://github.com/networkit/networkit/issues/787">https://github.com/networkit/networkit/issues/787</a> for
details.</p></li>
<li><p>Graph class: the algorithm to compute Kruskal Minimum Spanning Forest now uses the SpanningForest algorithm for undirected graphs. This leads to
a general performance improvement.</p></li>
<li><p>Deprecation of several Python modules: <code class="code docutils literal notranslate"><span class="pre">csbridge</span></code>, <code class="code docutils literal notranslate"><span class="pre">exceptions</span></code>, <code class="code docutils literal notranslate"><span class="pre">GEXFIO</span></code>, <code class="code docutils literal notranslate"><span class="pre">GraphMLIO</span></code>, <code class="code docutils literal notranslate"><span class="pre">partitioning</span></code>,
<code class="code docutils literal notranslate"><span class="pre">sampling</span></code>, <code class="code docutils literal notranslate"><span class="pre">stopwatch</span></code>, <code class="code docutils literal notranslate"><span class="pre">viztasks</span></code>, <code class="code docutils literal notranslate"><span class="pre">workflows</span></code>. Note that some functionalities are moved to other modules. See
documentation of further details.</p></li>
<li><p>Improvement of the Python documentation. Doc-strings now report input parameters, return values, and inheritance relationship.</p></li>
<li><p>Python APIs for the Maxent-Stress layout algorithm now support 3D coordinates.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span>:</dt><dd><ul class="simple">
<li><p>Fixed bug in the dynamic Dijkstra implementation (after an edge update, some distances were not updated correctly).</p></li>
</ul>
</dd>
</dl>
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<section id="march-14-2022-new-paper-using-networkit">
<h3>March 14, 2022: <strong>New paper using NetworKit</strong><a class="headerlink" href="#march-14-2022-new-paper-using-networkit" title="Link to this heading">¶</a></h3>
<p>The paper <a class="reference external" href="https://arxiv.org/abs/2203.01263">Interactive Visualization of Protein RINs using NetworKit in the Cloud</a> (authors: E. Angriman, F.
Brandt-Tumescheit, L. Franke, A. van der Grinten and H. Meyerhenke) has been accepted for IPDPS workshop on Graphs, Architectures, Programming, and
Learning (<a class="reference external" href="https://hpc.pnl.gov/grapl/">GrAPL 2022</a>). In the paper NetworKit is used for near realtime manipulation and visualization of protein
networks. A basic version of the visualization tool using <a class="reference external" href="https://plotly.com">Plotly</a> for generating 2D and 3D visualizations of networks will be
integrated in future releases.</p>
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<section id="february-8-2022-networkit-day-2022-march-3rd">
<h3>February 8, 2022: <strong>NetworKit Day 2022 - March 3rd</strong><a class="headerlink" href="#february-8-2022-networkit-day-2022-march-3rd" title="Link to this heading">¶</a></h3>
<p>Dear (prospective and current) NetworKit users and developers,</p>
<p>as already announced at a previous date - we are looking forward to a new NetworKit Day in 2022, taking place on March 3rd from 1 p.m. to 5 p.m. (CET)
online via Zoom. Registration is mandatory, but free of charge. This event is - as the previous ones - about interacting with the community. We share
our latest updates, give insights for new users and also offer two workshops: one for beginners and one for advanced users. If you want to attend one
or more workshops, better be prepared with a notebook and a modern webbrowser (although it is only for convenience, not a requirement). We also intend
to discuss future development directions and receive feedback on the current status of NetworKit. NetworKit Day will also feature one scientific talk
by Rob Kooij from TU Delft (Netherlands) about “Robustness of Complex Networks”.</p>
<p>The program of the event can be found on our NetworKit Day subpage.</p>
<p><span class="underline">Link for registration:</span> <a class="reference external" href="https://www.eventbrite.de/e/networkit-day-2022-nd22-registration-261084148717">https://www.eventbrite.de/e/networkit-day-2022-nd22-registration-261084148717</a></p>
<p>Looking forward to seeing you on March 3rd!</p>
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</section>
<section id="december-20-2021-save-the-date-networkit-day-2022">
<h3>December 20, 2021: <strong>Save the Date: NetworKit Day 2022</strong><a class="headerlink" href="#december-20-2021-save-the-date-networkit-day-2022" title="Link to this heading">¶</a></h3>
<p>We are happy to announce a new NetworKit Day. The event will take place on March, 3rd 2022 - starting at 1 p.m. and ending at 6 p.m CET. Details
concerning the program schedule will be shared at a later date.</p>
<p>Wish you all a good holiday season!</p>
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</section>
<section id="december-14-2021-networkit-9-1-1-released">
<h3>December 14, 2021: <strong>NetworKit 9.1.1 released</strong><a class="headerlink" href="#december-14-2021-networkit-9-1-1-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New features</span></dt><dd><ul class="simple">
<li><p>Wheels: NetworKit is now available as pre-built wheel-packages for nearly all supported platforms via pip. In case you prefer to build the C++
core and extensions, use <code class="code docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">--no-binary</span> <span class="pre">networkit</span></code>.</p></li>
<li><p>M1 macOS: support for install NetworKit as a native package via pip.</p></li>
</ul>
</dd>
</dl>
<p><span class="underline">New algorithms</span></p>
<dl class="simple">
<dt>Community:</dt><dd><ul class="simple">
<li><p>New overlapping community detection algorithm LFM (Local Fitness Method), available in <code class="code docutils literal notranslate"><span class="pre">centrality.LFM</span></code>. This is contribution from J.
Gelhausen (KIT Karlsruhe)</p></li>
<li><p>New parallel version of Leiden-based community detection algorithm, available in <code class="code docutils literal notranslate"><span class="pre">community.ParallelLeiden</span></code>. This is a contribution from
F. Nguyen (KIT Karlsruhe).</p></li>
</ul>
</dd>
<dt>GraphTools:</dt><dd><ul class="simple">
<li><p>New function topologicalSort: returns a list of nodes sorted by a valid topological ordering, available in <code class="code docutils literal notranslate"><span class="pre">graphtools.topologicalSort</span></code>.</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span></dt><dd><ul class="simple">
<li><p><code class="code docutils literal notranslate"><span class="pre">NetworkBinaryReader/Writer</span></code>: support for reading/writing edge indices and pickling graphs.</p></li>
<li><p>Improved performance for <code class="code docutils literal notranslate"><span class="pre">CSRMatrix</span></code> functions sort() and diagonal().</p></li>
<li><p>Improved performance for Vector function <code class="code docutils literal notranslate"><span class="pre">mean()</span></code>.</p></li>
<li><p>Improved performance for <code class="code docutils literal notranslate"><span class="pre">Graphbuilder</span></code> (only available in C++).</p></li>
<li><p>Improvements to the documentation, available at <a class="reference external" href="https://networkit.github.io/dev-docs/index.html">https://networkit.github.io/dev-docs/index.html</a></p></li>
<li><p>Support for clang-13.</p></li>
</ul>
</dd>
<dt><span class="underline">Notable Bug-Fixes</span></dt><dd><ul class="simple">
<li><p>Fixed a rare bug in <code class="code docutils literal notranslate"><span class="pre">centrality.GroupClosenessLocalSearch</span></code>, which could lead to worse solutions.</p></li>
<li><p>Fixed <code class="code docutils literal notranslate"><span class="pre">coloring.SpectralColoring()</span></code> by adjusting scipy-imports.</p></li>
<li><p>Fixed a problem for the experimental Windows support, where the wrong Python-libs are linked when multiple Python-versions are installed.</p></li>
</ul>
</dd>
</dl>
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<section id="july-1-2021-networkit-9-0-released">
<h3>July 1, 2021: <strong>NetworKit 9.0 released</strong><a class="headerlink" href="#july-1-2021-networkit-9-0-released" title="Link to this heading">¶</a></h3>
<dl class="simple">
<dt><span class="underline">New feature</span></dt><dd><ul class="simple">
<li><p>Windows 7, 8.1 and 10: possibility to install NetworKit via pip. Currently we have no pre-built wheel-package available so you have to make sure
that the MSVC-compiler (cl.exe) can be found when installing NetworKit via pip. A possible solution is to call “pip” from within “Native Tools
Command Prompt” provided by Visual Studio. This feature will be further improved in the future.</p></li>
</ul>
</dd>
</dl>
<p><span class="underline">New algorithms</span></p>
<dl class="simple">
<dt>Centrality:</dt><dd><ul class="simple">
<li><p>Greedy algorithm for group harmonic closeness based on “Group-Harmonic and Group-Closeness Maximization - Approximation and Engineering”, E.
Angriman, R. Becker, G. D’Angelo, H. Gilbert, A. van der Grinten, H. Meyerhenke, ALENEX 2021. This algorithm is available in
<code class="code docutils literal notranslate"><span class="pre">networkit.centrality.GroupHarmonicCloseness</span></code>.</p></li>
<li><p>Local search approximation algorithm for group closeness based on the aforementioned ALENEX 2021 paper. This algorithm is available in
<code class="code docutils literal notranslate"><span class="pre">networkit.centrality.GroupClosenessLocalSearch</span></code>.</p></li>
<li><p>Heuristic algorithm for group closeness (LS-Restrict) based on “Local Search for Group Closeness Maximization on Big Graphs”, E.Angriman, A. van
der Grinten, H. Meyerhenke, IEEE BigData 2019. This algorithm is available in <code class="code docutils literal notranslate"><span class="pre">networkit.centrality.GroupClosenessLocalSwaps</span></code>.</p></li>
<li><p>New algorithm for Normalized PageRank based on “Comparing Apples and Oranges: Normalized PageRank for Evolving Graphs”, K. Berberich, S.
Bedathur, G. Weikum, M. Vazirgiannis, WWW 2007. The algorithm is available in <code class="code docutils literal notranslate"><span class="pre">networkit.centrality.PageRank</span></code>.</p></li>
</ul>
</dd>
<dt>Community Detection:</dt><dd><ul class="simple">
<li><p>Based on Map Equation, available via <code class="code docutils literal notranslate"><span class="pre">networkit.community.LouvainMapEquation</span></code>. For further information about the algorithm, see “The map
equation”, M. Rosvall, D. Axelsson, C. T. Bergstrom, EPJ ST 2009.</p></li>
<li><p>Based on Overlapping Normalized Mutual Information, from the paper “Normalized Mutual Information to Evaluate Overlapping”, A. McDaid, D.
Greene, N. Hurley, Physics and Society 2011. This algorithm is available in <code class="code docutils literal notranslate"><span class="pre">networkit.community.OverlappingNMIDistance</span></code>.</p></li>
</ul>
</dd>
<dt>Matching:</dt><dd><ul class="simple">
<li><p>Suitor matcher, based on “New Effective Multithreaded Matching Algorithms”, F. Manne and M. Halappanavar, IPDPS 2014. This algorithm is
available in <code class="code docutils literal notranslate"><span class="pre">networkit.matching.SuitorMatcher</span></code>.</p></li>
</ul>
</dd>
<dt>GraphTools:</dt><dd><ul class="simple">
<li><p>New function <code class="code docutils literal notranslate"><span class="pre">subgraphFromNodes</span></code>: returns an induced subgraph based on an input graph</p></li>
<li><p>The previous <code class="code docutils literal notranslate"><span class="pre">subgraphFromNodes</span></code> has been renamed to <code class="code docutils literal notranslate"><span class="pre">subgraphAndNeighborsFromNodes</span></code> in order to better reflect its functionality</p></li>
</ul>
</dd>
<dt><span class="underline">Further changes and improvements</span></dt><dd><ul class="simple">
<li><p>Template implementation of CSRMatrix</p></li>
<li><p>Clang-analyzer warnings are fixed and treated as errors</p></li>
<li><p>Improved performance of graph writers</p></li>
<li><p>Possibility to try-out NetworKit without installation: binder support + cloud instances</p></li>
<li><p>Optimized memory usage in LAMG and ConjugateGradient</p></li>
<li><p>Improved runtime of (parallel) coarsening implementation for clusterings</p></li>
<li><p>Improved runtime of isProper() for matching</p></li>
<li><p>Support for clang-12 and gcc-11 compilers</p></li>
<li><p>AVX2 support for Windows</p></li>
</ul>
</dd>
</dl>
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<section id="february-23-2021-networkit-8-1-released">
<h3>February 23, 2021: <strong>NetworKit 8.1 released</strong><a class="headerlink" href="#february-23-2021-networkit-8-1-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">New features</span></p>
<ul class="simple">
<li><p>New embedding module that implements the node2vec algorithm based on “node2vec: Scalable feature learning for networks” by Grover and Leskovec (KDD
2016). The embedding module is available for both C++ and Python.</p></li>
<li><p>New csbridge Python module that allows to draw colored graphs inline in a jupyter notebook via ipycytoscape.</p></li>
<li><p>Better implementation of <code class="code docutils literal notranslate"><span class="pre">ClusterRandomGraphGenerator</span></code>: now it takes linear time and supports parallelism.</p></li>
<li><p>Added support for Binder. Newer branches from NetworKit can now be accessed directly from Binder. Currently supported are master (newest stable) and
8.1 (release version).</p></li>
</ul>
<p><span class="underline">For developers</span></p>
<ul class="simple">
<li><p>We raised the minimum required clang version from 3.8 to 3.9.</p></li>
<li><p>It is now possible to create the Python package against an external pre-build tlx-library. To use it, add <code class="code docutils literal notranslate"><span class="pre">--external-tlx=<TLX_PATH></span></code> to
<code class="code docutils literal notranslate"><span class="pre">setup.py</span> <span class="pre">build_ext-phase</span></code>.</p></li>
<li><p>All clang-tidy warnings have been resolved and will be treated as errors by our CI pipeline. Some of the clang-tidy checks also involve possible
performance enhancements and/or lowering of the memory footprint by avoiding unnecessary copies. The exact benefit depends on the use-case.</p></li>
<li><p>Several warning and documentation fixes.</p></li>
</ul>
<p><span class="underline">Notable bugfixes</span></p>
<ul class="simple">
<li><p>When using custom compilers on macOS (for example homebrew gcc compiler) and NetworKit was built from source with an external core, this created a
NetworKit installation with incompatible core and cython-extension libraries.</p></li>
<li><p>In <code class="code docutils literal notranslate"><span class="pre">KatzCentrality</span></code>, the parameter alpha was set to 0 by default. This caused the edges to be ignored and every node got the same centrality.</p></li>
</ul>
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<section id="january-15-2021-new-paper-using-networkit">
<h3>January 15, 2021: <strong>New paper using NetworKit</strong><a class="headerlink" href="#january-15-2021-new-paper-using-networkit" title="Link to this heading">¶</a></h3>
<p>The paper “New Approximation Algorithms for Forest Closeness Centrality - for Individual Vertices and Vertex Groups” (authors: van der Grinten,
Angriman, Predari, Meyerhenke) was selected for publication by <a class="reference external" href="https://www.siam.org/conferences/cm/conference/sdm21">SIAM Data Mining 2021</a>. In the
paper NetworKit is used for computing the experimental data. We also plan to include the new Forest Closeness Centrality algorithms in future
releases.</p>
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<section id="december-18-2020-networkit-8-0-released">
<h3>December 18, 2020: <strong>NetworKit 8.0 released</strong><a class="headerlink" href="#december-18-2020-networkit-8-0-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">New features</span></p>
<ul class="simple">
<li><p>Possibility to specify edge directions for Katz centrality</p></li>
<li><p>New algorithm to approximate Electrical Closeness, based on <a class="reference external" href="https://drops.dagstuhl.de/opus/volltexte/2020/12872/pdf/LIPIcs-ESA-2020-6.pdf">Approximation of the Diagonal of a Laplacian’s Pseudoinverse for Complex Network
Analysis</a> by E. Angriman, A. van der Grinten, M. Predari and H.
Meyerhenke</p></li>
<li><p>New algorithm: SPSP (Some Pairs Shortest Paths), as APSP but with user-specified source vertices</p></li>
</ul>
<p><span class="underline">New features for Contributors / Developers</span></p>
<ul class="simple">
<li><p>We moved our continious integration testing from Travis-CI to Github Actions. While the test-coverage stays the same, testing time is significantly
reduced. This results in faster feedback for your pull requests.</p></li>
<li><p>Based on our rule to support compilers which are 5 years old, the minimum support for gcc was raised to version 5.</p></li>
<li><p>NetworKit now support C++14 features.</p></li>
</ul>
<p><span class="underline">Further Improvements</span></p>
<ul class="simple">
<li><p>The documentation is improved and includes rendering-fixes, when dealing with certain elements like formulas.</p></li>
<li><p>Refactored <code class="code docutils literal notranslate"><span class="pre">Betweenness</span></code> and <code class="code docutils literal notranslate"><span class="pre">ApproxBetweenness</span></code>, leading to improved parallel performance.</p></li>
</ul>
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<section id="september-08-2020-networkit-7-1-released">
<h3>September 08, 2020: <strong>NetworKit 7.1 released</strong><a class="headerlink" href="#september-08-2020-networkit-7-1-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">New features for Contributors / Developers</span></p>
<ul class="simple">
<li><p>We restructured the Cython-Interface (responsible for the connection between Python and C++ core-libraries) in order to make development and
maintenance more approachable. As a result the previous monolithic file <code class="code docutils literal notranslate"><span class="pre">_NetworKit.pyx</span></code> is now split into modules, resembling the structure
of the C++ code. New modules can be added easily by providing appropriate Cython-files in sub-folder <a class="reference external" href="https://github.com/networkit/networkit/tree/master/networkit">networkit</a>.</p></li>
</ul>
<p><span class="underline">Further Improvements</span></p>
<ul class="simple">
<li><p>Refactored the <cite>EdgeListReader</cite>, leading to a speed-up when reading in edge-list based graph files.</p></li>
</ul>
<p><span class="underline">Additional Notes</span></p>
<ul class="simple">
<li><p>Beginning with release <code class="code docutils literal notranslate"><span class="pre">7.1</span></code> (<code class="code docutils literal notranslate"><span class="pre">7.0</span></code> also available) NetworKit is now also distributed via package managers conda, spack and brew. All
channels provide different packages for the C++ headers/library and the complete Python/C++ software. Head over to <a class="reference external" href="https://github.com/networkit/networkit">github</a> for installation instructions.</p></li>
</ul>
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<section id="may-29-2020-networkit-7-0-released">
<h3>May 29, 2020: <strong>NetworKit 7.0 released</strong><a class="headerlink" href="#may-29-2020-networkit-7-0-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">New Features</span></p>
<ul class="simple">
<li><p>New algorithms for GedWalk centrality based on the paper <a class="reference external" href="https://arxiv.org/abs/1910.13874">Group Centrality Maximization for Large-scale Graphs</a>
(ALENEX 2020).</p></li>
<li><p>New parallel implementation of the <a class="reference external" href="https://www.ijcai.org/Proceedings/16/Papers/525.pdf">Hayashi et al. algorithm</a> for spanning edge centrality
approximation.</p></li>
<li><p>PageRank: possibility to choose between the L1 and the L2 norms as stopping criterion of the algorithm, and to set a maximum number of iterations.</p></li>
<li><p>GlobalThresholdFilter: support for weighted and directed graphs.</p></li>
</ul>
<p><span class="underline">Notable Bugfixes</span></p>
<ul class="simple">
<li><p>CommuteTimeDistance now returns the correct distance between two nodes for computation with and without preprocessing</p></li>
<li><p>Fix of an error in the <code class="code docutils literal notranslate"><span class="pre">exportGraph</span></code>-function of GephiStreaming</p></li>
<li><p>Fix of an error in APSP that returned wrong distances in disconnected graphs</p></li>
</ul>
<p><span class="underline">Further Improvements</span></p>
<ul class="simple">
<li><p>Support for newer Python-version: 3.8</p></li>
<li><p>Support for newer compiler: gcc 10.1, AppleClang 11.03</p></li>
<li><p>Reduce memory footprint of several functions/classes: BFS, Dijkstra, SSSP, TopCloseness</p></li>
<li><p>Reduce time-complexity of several functions/classes: GephiStreamer, StaticDegreeSequenceGenerator, TopCloseness, WattsStrogatzGenerator</p></li>
<li><p>Added more notebook as examples</p></li>
</ul>
<p><span class="underline">Additional Notes for Contributors Developers</span></p>
<ul class="simple">
<li><p>Development will be done on the master branch, the Dev branch will not be used anymore.</p></li>
</ul>
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<section id="march-2020-new-accepted-papers-using-networkit">
<h3>March 2020: <strong>new accepted papers using NetworKit</strong><a class="headerlink" href="#march-2020-new-accepted-papers-using-networkit" title="Link to this heading">¶</a></h3>
<ul class="simple">
<li><p>“Scaling up Network Centrality Computations - a Brief Overview” was accepted for publishing in the journal <a class="reference external" href="https://www.degruyter.com/view/journals/itit/62/1/itit.62.issue-1.xml">it - Information Technology</a>.</p></li>
<li><p>“Scaling Betweenness Approximation to Billions of Edges by MPI-based Adaptive Sampling” accepted for <a class="reference external" href="http://www.ipdps.org">IPDPS 2020</a>.</p></li>
</ul>
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<section id="march-1-2020-networkit-6-1-0-released">
<h3>March 1, 2020: <strong>NetworKit 6.1.0 released</strong><a class="headerlink" href="#march-1-2020-networkit-6-1-0-released" title="Link to this heading">¶</a></h3>
<p>In the following you see an overview about the contributions, which went into NetworKit 6.1.0. Note that this version is fully compatible with release
6.0.0.</p>
<p><span class="underline">New features</span></p>
<ul class="simple">
<li><p>Introducing new iterators for nodes and edges to have a coherent, idiomatic and fast way to repeate tasks for different elements of a graph.
Syntax-wise the iterators can be called similarly in Python and C++. In Python iterating can be invoked by <code class="code docutils literal notranslate"><span class="pre">for</span> <span class="pre">x</span> <span class="pre">in</span> <span class="pre">graph.iterNodes()</span></code>,
whereas the counter-part for C++ works with <code class="code docutils literal notranslate"><span class="pre">for(node</span> <span class="pre">x:</span> <span class="pre">graph.nodeRange())</span></code>. Internally, all functions in NetworKit already use the new
iterators.</p></li>
<li><p>cmake adds more options to support variants of clang-compilers with OpenMP for macOS and Linux. This includes conda, homebrew and
MacPort-environments.</p></li>
</ul>
<p><span class="underline">Bugfixes</span></p>
<ul class="simple">
<li><p>Generating a graph with the Watts-Strogatz algorithm does not lead anymore to infinite loops, when passing a number of neighbors per node, which is
equal to the total number of nodes in the graph. (See issue <a class="reference external" href="https://github.com/networkit/networkit/issues/505">#505</a>)</p></li>
<li><p>Fixed error in function inNeighbors, including not all parameters in call to underlying library. (See issue <a class="reference external" href="https://github.com/networkit/networkit/issues/469">#469</a>)</p></li>
<li><p>The z-coordinate is now correctly scaled when writing a graph to GML. (See issue <a class="reference external" href="https://github.com/networkit/networkit/issues/500">#500</a>)</p></li>
<li><p>ConnectedComponents::extractLargestConnectedComponent now returns a compacted graph if called with appropriate parameters.</p></li>
</ul>
<p><span class="underline">Deprecated features</span></p>
<ul class="simple">
<li><p>Nested-parallelism-feature is now marked as deprecated.</p></li>
</ul>
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<section id="february-24-2020-networkit-6-0-1-released">
<h3>February 24, 2020: <strong>NetworKit 6.0.1 released</strong><a class="headerlink" href="#february-24-2020-networkit-6-0-1-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">Patch notes</span></p>
<ul class="simple">
<li><p>Added an option to cmake (-DNETWORKIT_EXT_TLX), which enables to link against an externally built tlx-library</p></li>
<li><p>Updated travis-configuration in order to remove deprecated options</p></li>
<li><p>Fixed a <a class="reference external" href="https://github.com/networkit/networkit/issues/491">bug</a>, which prevented the headers from ttmath to be installed correctly</p></li>
</ul>
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<section id="november-29-2019-networkit-6-0-released">
<h3>November 29, 2019: <strong>NetworKit 6.0 released</strong><a class="headerlink" href="#november-29-2019-networkit-6-0-released" title="Link to this heading">¶</a></h3>
<p><span class="underline">New features</span></p>
<ul class="simple">
<li><p>NetworKit binary graphs: new binary graph format that is both smaller usually smaller than text-based formats and also faster to read. The format
allows for parallel reading. It supports (un-)directed as well as (un-)weighted graphs and deleted nodes.</p></li>
<li><p>KadabraBetweenness: implementation of a new parallel algorithm for betweenness approximation. This is based on the definition from “Parallel
Adaptive Sampling with almost no Synchronization”, A. van der Grinten, E. Angriman, H. Meyerhenke</p></li>
<li><p>New method in ConnetedComponents to extract the largest connected component of a given graph.</p></li>
<li><p>BidirectionalBFS and BidirectionalDijkstra: new algorithms for faster graph exploration when the target vertex is known.</p></li>
<li><p>New method in Graph to remove all duplicate edges (i.e. additional edges with same source and same target as another edge).</p></li>
<li><p>New notebooks with tutorials for Centrality, Community detection, Components, Distance, Generators, Graph, Graph read/write, Randomization.</p></li>
<li><p>Removal of deprecated features (see list below for more informations)</p></li>
<li><p>New release cycle and version numbering: NetworKit now releases a major release every half a year, and an optional minor release every quarter. See
you in summer 2020 for NetworKit 7.0 then.</p></li>
<li><p>Package Manager support: conda, spack, brew and more packages will be created starting with 6.0. They will follow the github/PyPI-release in the
coming weeks.</p></li>
</ul>
<p><span class="underline">New features for developers</span></p>
<ul class="simple">
<li><p>Clang format: new .clang-format configuration file to format NetworKit C++ files.</p></li>
<li><p>Header files: all C++ header files have been moved to the include/ directory.</p></li>
</ul>
<p><span class="underline">Notable bugfixes</span></p>
<ul class="simple">
<li><p>“make install” and “ninja install” now correctly install the NetworKIt C++ library together with its header files. The pkg-config utility is
supported to link against the library.</p></li>
<li><p>NetworKit now always logs to stderr instead of stdout (regardless of the log level). This change makes life easier for programs that link against
NetworKit as a library but also need to adhere to a specific output format on stdout.</p></li>
<li><p>ApproxGroupBetweenness now uses much less memory and can scale to larger graphs.</p></li>
</ul>
<p><span class="underline">Deprecated features</span></p>
<ul class="simple">
<li><p>The following Graph methods have been deprecated: getId, typ, setName, getName, toString, nodes, edges, neighbors, time, timeStep.</p></li>
<li><p>The following Graph methods have been deprecated and moved to GraphTools: copyNodes, subgraphFromNodes, transpose, BFSfrom, DFSfrom. toUnweighted,
toUndirected, append, merge, volume</p></li>
<li><p>A deprecated constructor of the KONECTGraphReader class has been removed.</p></li>
<li><p>The deprecated FrutchermanReingold, and MultilevelLayouter algorithms have been removed.</p></li>
<li><p>The deprecated MaxClique algorithm has been removed.</p></li>
<li><p>The deprecated SSSP::getStack() method has been removed.</p></li>
<li><p>The following deprecated methods in Graph have been removed: addNode(float, float), setCoordinate, getCoordinate, minCoordinate, maxCoordinate,
initCoordinate</p></li>
</ul>
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<section id="november-2019-new-accepted-papers-using-networkit">
<h3>November 2019: new accepted papers using NetworKit<a class="headerlink" href="#november-2019-new-accepted-papers-using-networkit" title="Link to this heading">¶</a></h3>
<ul class="simple">
<li><p>“Local Search for Group Closeness Maximization on Big Graphs”, accepted for <a class="reference external" href="http://bigdataieee.org/BigData2019/">IEEE BigData 2019</a>.</p></li>
<li><p>“Group Centrality Maximization for Large-scale Graphs” accepted for <a class="reference external" href="https://www.siam.org/conferences/cm/conference/alenex20">ALENEX 2020</a>.</p></li>
<li><p>“Guidelines for Experimental Algorithmics: A Case Study in Network Analysis” was accepted and published by the open-access journal <em>Algorithms</em>. It
is part of the Special Issue: “Algorithm Engineering: Towards Practically Efficient Solutions to Combinatorial” edited by Daniele Frigioni and
Mattia D’Emidio. More information can be found here: <a class="reference external" href="https://www.mdpi.com/1999-4893/12/7/127">https://www.mdpi.com/1999-4893/12/7/127</a>.</p></li>
<li><p>“Parallel Adaptive Sampling with almost no Synchronization” accepted for <a class="reference external" href="https://2019.euro-par.org/">Euro-Par 2019</a>.</p></li>
<li><p>“Scalable Katz Ranking Computation in Large Static and Dynamic Graphs” accepted for <a class="reference external" href="http://algo2018.hiit.fi/esa/">Esa 2018</a>.</p></li>
<li><p>“Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades” accepted for <a class="reference external" href="http://algo2018.hiit.fi/esa/">Esa 2018</a>.</p></li>
<li><p>“The Polynomial Volume Law of Complex Networks in the Context of Local and Global Optimization” in <a class="reference external" href="https://www.nature.com/articles/s41598-018-29131-0">Scientific Reports</a>.</p></li>
<li><p>“Computing Top-k Closeness Centrality in Fully-dynamic Graphs” accepted for <a class="reference external" href="https://archive.siam.org/meetings/alenex18/">ALENEX 2018</a>.</p></li>
</ul>
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<section id="december-19-2018-networkit-5-0-released">
<h3>December 19, 2018: <strong>NetworKit 5.0 released</strong><a class="headerlink" href="#december-19-2018-networkit-5-0-released" title="Link to this heading">¶</a></h3>
<p>Major features:</p>
<ul class="simple">
<li><p>New algorithm for approximating of the betweenness centrality of all the nodes of a graph or of the top-k nodes with highest betweenness centrality
based on: “KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation”, M. Borassi, E. Natale. Presented at ESA 2016.</p></li>
<li><p>New Mocnik graph generator based on: “Modelling Spatial Structures”, F.B. Mocnik, A. Frank. Presented at COSIT 2015.</p></li>
<li><p>New build system based on CMake.</p></li>
<li><p>Support for C++ build on Windows.</p></li>
</ul>
<p>Minor changes:</p>
<ul class="simple">
<li><p>Parallel Erdos Reny graph generator.</p></li>
<li><p>NetworKit installation via pip: missing packages will be automatically downloaded.</p></li>
<li><p>Partition: equality between partitions can be quickly checked via hashing.</p></li>
<li><p>Closeness: generalized definition of Closeness centrality so it can be computed also on disconnected graphs.</p></li>
<li><p>Aux::PrioQueue allows read access to its elements via iterators.</p></li>
<li><p>Graph class: new reductions allow to compute the maximum (weighted) degree of a graph in parallel.</p></li>
</ul>
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<section id="june-25-2018-networkit-4-6-released">
<h3>June 25, 2018: <strong>NetworKit 4.6 released</strong><a class="headerlink" href="#june-25-2018-networkit-4-6-released" title="Link to this heading">¶</a></h3>
<p>Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with
performance-critical algorithms implemented in C++/OpenMP.</p>
<p><strong>Release notes</strong></p>
<p>Major features:</p>
<ul class="simple">
<li><p>Dynamic algorithm for keeping track of k nodes with highest closeness centrality (based on “Computing Top-k Closeness Centrality in Fully-dynamic
Graphs”, P. Bisenius, E. Bergamini, E. Angriman and H. Meyerhenke. Presented at ALENEX 2018).</p></li>
<li><p>Dynamic algorithm to keep track of k nodes with highest Katz centrality (based on “Scalable Katz Ranking Computation in Large Static and Dynamic
Graphs”, A. van der Grinten, E. Bergamini, O. Green, D. A. Bader and H. Meyerhenke.).</p></li>
<li><p>Curveball graph randomization algorithm based on “Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades”, C. J.
Carstens, M. Hamann, U. Meyer, M. Penschuck, H. Tran and D. Wagner.</p></li>
<li><p>Algorithm for finding the group of nodes with highest betweenness centrality (based on “Scalable Betweenness Centrality Maximization via Sampling”,
A. Mahmoody, C. E. Tsourakakis, E. Upfal).</p></li>
<li><p>Algorithm for finding the group of nodes with highest group degree based on the definition in “The Centrality of Groups and Classes”, M.G. Everett,
S.P. Borgatti.</p></li>
<li><p>Algorithm for finding all the biconnected components of a graph based on “Algorithm 447: efficient algorithms for graph manipulation”, J. Hopcroft,
R. Tarjan.</p></li>
<li><p>Support for binary graph I/O: Support for graphs exported by Thrill (see <a class="reference external" href="https://github.com/thrill/thrill">https://github.com/thrill/thrill</a>), and Implementation of binary partition
readers and writers that are potentially faster than their text-based counterparts.</p></li>
</ul>
<p>Minor changes:</p>
<ul class="simple">
<li><p>All algorithms for finding the top-k (harmonic) closeness can also return all the nodes whose centrality is equal to the k-th highest. This
behaviour can be triggered by parameter passed in the constructor of the class.</p></li>
<li><p>Faster KONECT and SNAP graph readers: roughly 2x speedup on the previous readers.</p></li>
<li><p>Greatly improved running time of NetworKit’s unit tests.</p></li>
<li><p>Size reduction of the “input” folder. In case of space constraints, we suggest to do a shallow clone of the NetworKit repository: git clone
–depth=1 <a class="reference external" href="http://github.com/networkit/networkit">http://github.com/networkit/networkit</a></p></li>
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<section id="december-14-2017-networkit-4-5-released">
<h3>December 14, 2017: <strong>NetworKit 4.5 released</strong><a class="headerlink" href="#december-14-2017-networkit-4-5-released" title="Link to this heading">¶</a></h3>
<p>Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with
performance-critical algorithms implemented in C++/OpenMP.</p>
<p><strong>Release notes</strong></p>
<p>Major:</p>
<ul class="simple">
<li><p>Algorithm for finding the group of nodes with highest closeness centrality (based on “Scaling up Group Closeness Maximization”, E. Bergamini, T.
Gonser and H. Meyerhenke. To appear at ALENEX 2018).</p></li>
<li><p>Dynamic algorithm for updating the betweenness of a single node faster than updating it for all nodes (based on “Improving the betweenness
centrality of a node by adding links”, E. Bergamini, P. Crescenzi, G. D’Angelo, H. Meyerhenke, L. Severini and Y. Velaj. Accepted by JEA).</p></li>
<li><p>Dynamic algorithm for keeping track of k nodes with highest closeness centrality (based on “Computing Top-k Closeness Centrality in Fully-dynamic
Graphs”, P. Bisenius, E. Bergamini, E. Angriman and H. Meyerhenke. To appear at ALENEX 2018).</p></li>
</ul>
<p>Minor:</p>
<ul class="simple">
<li><p>Dynamic algorithm for updating the weakly connected components of a directed graph after edge additions or removals.</p></li>
<li><p>Official support for Windows 10. Take a look at the <a class="reference external" href="https://networkit.github.io/get_started.html">Get Started guide</a> for further instructions.</p></li>
<li><p>Support for SCons3. There are no more dependencies on Python 2 if you decide to use SCons3 with Python 3.</p></li>
<li><p>Improved include of external libraries. These can now simply be specified in the build.conf file. See <a class="reference external" href="https://github.com/networkit/networkit/pull/58">Pull Request #58</a> for further details.</p></li>
</ul>
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<section id="september-06-2017-networkit-4-4-released">
<h3>September 06, 2017: <strong>NetworKit 4.4 released</strong><a class="headerlink" href="#september-06-2017-networkit-4-4-released" title="Link to this heading">¶</a></h3>
<p>Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with
performance-critical algorithms implemented in C++/OpenMP.</p>
<p><strong>Release notes</strong></p>
<p>Major:</p>
<ul class="simple">
<li><p>Weakly connected components (components.WeaklyConnectedComponents)</p></li>
<li><p>Dynamic algorithm for updating connected components in undirected graphs (components.DynConnectedComponents)</p></li>
<li><p>Algorithm for computing the weakly connected components in directed graphs (components.WeaklyConnectedComponents)</p></li>
<li><p>Enumeration of all simple paths between two nodes, up to a user-specified threshold (distance.AllSimplePaths)</p></li>
</ul>
<p>Minor:</p>
<ul class="simple">
<li><p>Improved documentation</p></li>
<li><p>Marked SSSP::getStack() as deprecated and replaced with SSSP::getNodesSortedByDistance()</p></li>
<li><p>Several fixes in the LFR generator</p></li>
<li><p>Added a wrapper class for the BTER implementation FEASTPACK</p></li>
<li><p>Expose restoreNode method to Python</p></li>
<li><p>Added shared library option to SCons</p></li>
</ul>
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<section id="july-19-2017-networkit-day-on-september-12-2017">
<h3>July 19, 2017: <strong>NetworKit Day</strong> on September 12, 2017<a class="headerlink" href="#july-19-2017-networkit-day-on-september-12-2017" title="Link to this heading">¶</a></h3>
<p>The first NetworKit Day will be held on September 12, 2017 at the Karlsruhe Institute of Technology, Karlsruhe, Germany. For further information,
visit the webpage <a class="reference external" href="https://networkit.github.io/networkit-day.html">https://networkit.github.io/networkit-day.html</a></p>
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<section id="june-07-2017-networkit-4-3-released">
<h3>June 07, 2017: <strong>NetworKit 4.3 released</strong><a class="headerlink" href="#june-07-2017-networkit-4-3-released" title="Link to this heading">¶</a></h3>
<p>Today we announce the next version of NetworKit, the open-source toolkit for large-scale network analysis. NetworKit is a Python package, with
performance-critical algorithms implemented in C++/OpenMP.</p>
<p><strong>Release notes</strong></p>
<p>Major:</p>
<ul class="simple">
<li><p>New dynamic algorithm for updating exact betweenness centrality after an edge insertion, based on “Faster Betweenness Centrality Updates in Evolving
Networks”, Bergamini et al., to appear at SEA 2017 (<a class="reference external" href="https://arxiv.org/abs/1704.08592">https://arxiv.org/abs/1704.08592</a>)</p></li>
<li><p>New dynamic algorithm for updating APSP after an edge insertion (this is basically the first step of the dynamic betweenness algorithm, with the
difference that only distances are updated, and not the number of shortest paths)</p></li>
<li><p>New faster algorithm for listing all maximal cliques, based on “Listing All Maximal Cliques in Large Sparse Real-World Graphs”, Eppstein and Strash,
SEA 2011 (<a class="reference external" href="https://link.springer.com/chapter/10.1007/978-3-642-20662-7_31">https://link.springer.com/chapter/10.1007/978-3-642-20662-7_31</a>)</p></li>
</ul>
<p>Minor:</p>
<ul class="simple">
<li><p>New base class DynAlgorithm with a common interface for all dynamic algorithms.</p></li>
<li><p>Jupyter Notebook explaining how to use dynamic algorithms in NetworKit.</p></li>
<li><p>Renamed ApproxBetweenness2 to EstimateBetweenness.</p></li>
<li><p>Moved SSSP, DynSSSP and subclasses to distance module.</p></li>
<li><p>Refactored PrioQueue and PrioQueueForInts to have a common interface.</p></li>
<li><p>Made deletion of incident edges automatic when deleting a node.</p></li>
<li><p>Fixed minor issues and improved documentation of several classes.</p></li>
<li><p>Exported Graph::randomEdge(s) to Python.</p></li>
<li><p>Marked IndependentSetFinder, FruchtermanReingold, Layouter, MultilevelLayouter, RandomSpanningTree, PseudoRandomSpanningTree and MaxClique as
deprecated.</p></li>
</ul>
<p>NOTE: The classes marked as deprecated will be permanently deleted with the next release. Please contact us if there are reasons why some of the
classes should be kept.</p>
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