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Implement plan to organise repo
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‎Graph Analysis/Path_Analysis/README.md‎

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# Path Analysis
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Analyze JSON structure and field relationships.
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## JSON Path Structure
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- Script: `path_analysis_report.py`
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- Output: `reports/path_analysis_report.md`
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- Run:
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```bash
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python "Graph Analysis/Path_Analysis/path_analysis_report.py"
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```
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## Field Centrality (Co-occurrence)
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- Script: `Centrality_Analysis/json_centrality_analysis.py`
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- Output: `reports/centrality_analysis_report.md`
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- Run:
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```bash
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python "Graph Analysis/Path_Analysis/Centrality_Analysis/json_centrality_analysis.py"
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```
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Reports are written to the `reports/` directory.

‎Graph Analysis/README.md‎

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# Graph Analysis
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Analysis tools operating on meeting JSON data to derive structure and relationships.
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## Degree Analysis (Co-attendance)
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Script: `Degree_Analysis/degree_analysis_to_md.py`
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- Finds participant string lists recursively and builds an undirected co-attendance graph.
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- Computes degree per node and distribution, and writes a Markdown report.
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- Output: `Graph Analysis/Degree_Analysis/degree_analysis_report.md`
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- Run:
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```bash
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python "Graph Analysis/Degree_Analysis/degree_analysis_to_md.py"
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```
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## Path Analysis
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Script: `Path_Analysis/path_analysis_report.py`
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- Extracts all JSON paths (dot/array notation), computes depth statistics, and writes a report.
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- Output: `reports/path_analysis_report.md`
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- Run:
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```bash
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python "Graph Analysis/Path_Analysis/path_analysis_report.py"
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```
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## Centrality (Field Co-occurrence)
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Script: `Path_Analysis/Centrality_Analysis/json_centrality_analysis.py`
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- Builds a field co-occurrence graph and computes degree/betweenness/closeness/eigenvector centralities.
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- Output: `reports/centrality_analysis_report.md`
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- Run:
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```bash
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python "Graph Analysis/Path_Analysis/Centrality_Analysis/json_centrality_analysis.py"
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```
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See `reports/README.md` for example outputs.

‎Makefile‎

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PY=python
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setup:
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python3 -m venv .venv
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. .venv/bin/activate && $(PY) -m pip install --upgrade pip && pip install -r requirements.txt
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graphs:
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$(PY) Scripts/Nodes-Edges.py
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$(PY) Scripts/Nodes-Edges2.py
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gexf:
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$(PY) Scripts/GEXF-export.py
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summary:
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$(PY) Scripts/Import-JSON.py
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degree-analysis:
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$(PY) "Graph Analysis/Degree_Analysis/degree_analysis_to_md.py"
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path-report:
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$(PY) "Graph Analysis/Path_Analysis/path_analysis_report.py"
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centrality-report:
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$(PY) "Graph Analysis/Path_Analysis/Centrality_Analysis/json_centrality_analysis.py"

‎README.md‎

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# Graph-Python-scripts
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This repository contains Python scripts for parsing meeting summaries and visualizing them as directed graphs using NetworkX and Matplotlib, plus exporting to GEXF for use in Gephi.
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## Contents
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- `Scripts/Nodes-Edges.py`: Build a directed graph from a single meeting summary and save `graph.png`.
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- `Scripts/Nodes-Edges2.py`: Build a directed graph from multiple meeting summaries and save `graph2.png`.
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- `Scripts/GEXF-export.py`: Build a comprehensive directed graph and export `all_workgroups_graph_sanitized.gexf`.
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- `Scripts/count.py`: Print top-level workgroup names from the remote JSON.
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- `Scripts/Import-JSON.py`: Generate `workgroup_meetings_summary.txt` with counts and meeting listings per workgroup.
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- `graph.png`, `graph2.png`: Example rendered graphs.
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- `Scripts/all_workgroups_graph_sanitized.gexf`: Example GEXF export ready for Gephi.
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## Prerequisites
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- Python 3.9+
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- Packages:
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- `requests`
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- `networkx`
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- `matplotlib`
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Install packages:
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Scripts to fetch meeting summaries, generate graphs, and produce analysis reports.
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## Quickstart
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1. Create a virtual environment and install dependencies:
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```bash
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pip install requests networkx matplotlib
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python3 -m venv .venv
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source .venv/bin/activate
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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```
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## Data Source
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All scripts fetch JSON from a public URL:
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- `https://raw.githubusercontent.com/SingularityNET-Archive/SingularityNET-Archive/refs/heads/main/Data/Snet-Ambassador-Program/Meeting-Summaries/2025/meeting-summaries-array.json`
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The scripts handle both top-level list and dict JSON structures.
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## Usage
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Run scripts from the project root or the `Scripts/` directory.
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### 1) Build a graph from a single meeting: `Scripts/Nodes-Edges.py`
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- Purpose: Parse one meeting object and produce a directed graph of relationships among workgroup, meeting, people, documents, agenda items, action items, decision items, tags, and emotions.
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- Output: Saves an image `graph.png` in the project root.
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Command:
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2. Run common tasks:
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- Single-meeting graph → saves `graph.png`:
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```bash
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python Scripts/Nodes-Edges.py
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```
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### 2) Build a graph from multiple meetings: `Scripts/Nodes-Edges2.py`
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- Purpose: Iterate all meetings/workgroups and build a combined directed graph.
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- Output: Saves `graph2.png` in the project root.
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Command:
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- Multi-meeting graph → saves `graph2.png`:
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```bash
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python Scripts/Nodes-Edges2.py
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```
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### 3) Export a GEXF for Gephi: `Scripts/GEXF-export.py`
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- Purpose: Build a comprehensive, sanitized directed graph across all workgroups and export to GEXF.
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- Output: Writes `Scripts/all_workgroups_graph_sanitized.gexf`.
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Notes:
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- Ensures node IDs are strings and unique.
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- Sanitizes node/edge attributes to primitive types for GEXF compatibility.
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- Emits diagnostics (counts, sample nodes) before/after sanitization.
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Command:
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- Export Gephi file → writes `Scripts/all_workgroups_graph_sanitized.gexf`:
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```bash
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python Scripts/GEXF-export.py
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```
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Open the GEXF in Gephi to explore the network.
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### 4) Quick inspection of workgroups: `Scripts/count.py`
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- Purpose: Print the `workgroup` field for each top-level item in the JSON (list or dict).
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Command:
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- Workgroup summary → writes `Scripts/workgroup_meetings_summary.txt`:
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```bash
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python Scripts/count.py
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python Scripts/Import-JSON.py
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```
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### 5) Generate a text summary: `Scripts/Import-JSON.py`
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- Purpose: Create `workgroup_meetings_summary.txt` listing counts per workgroup and meeting info (date + title/type).
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- Output: `Scripts/workgroup_meetings_summary.txt`.
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Command:
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- Degree (co-attendance) analysis → writes `Graph Analysis/Degree_Analysis/degree_analysis_report.md`:
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```bash
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python Scripts/Import-JSON.py
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python "Graph Analysis/Degree_Analysis/degree_analysis_to_md.py"
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```
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- JSON path structure report → writes `reports/path_analysis_report.md`:
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```bash
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python "Graph Analysis/Path_Analysis/path_analysis_report.py"
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```
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- Field centrality report → writes `reports/centrality_analysis_report.md`:
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```bash
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python "Graph Analysis/Path_Analysis/Centrality_Analysis/json_centrality_analysis.py"
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```
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## Outputs
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- `graph.png`: Single-meeting graph.
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- `graph2.png`: Multi-meeting graph.
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- `Scripts/all_workgroups_graph_sanitized.gexf`: GEXF for Gephi.
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- `Scripts/workgroup_meetings_summary.txt`: Human-readable summary.
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## Repository Map
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- `Scripts/` — data fetching and basic graph generation. See `Scripts/README.md`.
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- `Graph Analysis/` — analysis utilities (degree, path, centrality). See `Graph Analysis/README.md`.
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- `reports/` — generated Markdown reports. See `reports/README.md`.
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To preview generated images on macOS/Linux:
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## Data Source
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All scripts read from a shared public JSON:
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`https://raw.githubusercontent.com/SingularityNET-Archive/SingularityNET-Archive/refs/heads/main/Data/Snet-Ambassador-Program/Meeting-Summaries/2025/meeting-summaries-array.json`
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```bash
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$BROWSER graph.png
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$BROWSER graph2.png
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```
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## Outputs
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- `graph.png`, `graph2.png` — rendered graphs
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- `Scripts/all_workgroups_graph_sanitized.gexf` — Gephi import
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- Markdown reports in `reports/`
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## Notes
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- All scripts are designed to run headlessly; graphs are saved to files instead of opening GUI windows.
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- The code uses defensive accessors and type checks to tolerate missing or differently shaped fields.
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- For reproducible layouts, the drawing functions use a fixed seed for `spring_layout` where applicable.
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Notes: Scripts run headlessly and save files to disk; images can be opened via your OS default viewer.

‎Scripts/README.md‎

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# Scripts
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Utilities for fetching meeting data and generating graphs/exports.
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## Nodes-Edges.py
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- Purpose: Build a directed graph from a single meeting entry and save an image.
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- Output: `graph.png`
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- Run:
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```bash
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python Scripts/Nodes-Edges.py
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```
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## Nodes-Edges2.py
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- Purpose: Build a combined directed graph across all meetings and save an image.
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- Output: `graph2.png`
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- Run:
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```bash
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python Scripts/Nodes-Edges2.py
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```
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## GEXF-export.py
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- Purpose: Build a comprehensive directed graph and export to GEXF (Gephi).
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- Output: `Scripts/all_workgroups_graph_sanitized.gexf`
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- Run:
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```bash
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python Scripts/GEXF-export.py
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```
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## Import-JSON.py
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- Purpose: Generate a text summary of workgroups and meetings.
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- Output: `Scripts/workgroup_meetings_summary.txt`
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- Run:
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```bash
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python Scripts/Import-JSON.py
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```
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## count.py
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- Purpose: Print the `workgroup` value for each top-level JSON item.
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- Run:
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```bash
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python Scripts/count.py
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```
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All scripts fetch JSON from the shared data source referenced in the top-level README.

‎reports/README.md‎

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# Reports
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Generated Markdown reports and their sources.
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- `workgroup_analysis_report.md`
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- Source: `Scripts/Import-JSON.py` (summary of workgroups and meetings)
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- `path_analysis_report.md`
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- Source: `Graph Analysis/Path_Analysis/path_analysis_report.py`
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- `centrality_analysis_report.md`
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- Source: `Graph Analysis/Path_Analysis/Centrality_Analysis/json_centrality_analysis.py`
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- `degree_analysis_by_field_with_interpretation.md`
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- Source: Analysis variants under `Graph Analysis/`
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- `Graph Analysis/Degree_Analysis/degree_analysis_report.md`
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- Source: `Graph Analysis/Degree_Analysis/degree_analysis_to_md.py`
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Re-run the corresponding scripts to regenerate any report.

‎requirements.txt‎

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requests>=2.32
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networkx>=3.2
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matplotlib>=3.8

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