Create Degree Analysis Script

Author: stephen-rowan

degree_analysis_report.md

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+# Degree Analysis Report
+**Generated on:** 2025-10-13 12:49:38
+
+## Summary Statistics
+- Total Nodes: 418
+- Maximum Degree: 18
+- Minimum Degree: 1
+
+## Top 10 Nodes by Degree
+| Rank | Node | Degree |
+|------|-------|---------|
+| 1 | We established the initial Q3 monthly roadmap and base expectations of what we want to achieve in sessions. | 18 |
+| 2 | Best places to find info: Discord, Meeting Summaries and Quarterly Reports | 18 |
+| 3 | All Categories are updated with new information | 18 |
+| 4 | What are Quarter 3 maintenance plans? | 18 |
+| 5 | GPT is very helpful, providing useful information or triggering thoughts of improvements | 18 |
+| 6 | Knowledge Base Proposal seems to be a good document for onboarding | 18 |
+| 7 | Uncertain when the time comes to contribute | 18 |
+| 8 | We went over the Progress Report, line by line | 18 |
+| 9 | Reviewed GitBook, shared update on the [getting started page] (https://ambassadorss-organization.gitbook.io/knowledge-base) | 18 |
+| 10 | It's time-consuming to learn about the document context and understand its current relevance for GitBook | 18 |
+
+## Degree Distribution
+| Degree | Count of Nodes |
+|---------|----------------|
+| 1 | 14 |
+| 2 | 30 |
+| 3 | 56 |
+| 4 | 70 |
+| 5 | 61 |
+| 6 | 48 |
+| 7 | 32 |
+| 9 | 12 |
+| 10 | 22 |
+| 11 | 13 |
+| 12 | 13 |
+| 13 | 28 |
+| 18 | 19 |

degree_analysis_to_md.py

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+import json
+import requests
+import networkx as nx
+from itertools import combinations
+from collections import Counter
+from datetime import datetime
+
+def load_json_remote(url):
+    """Load JSON data from a remote URL."""
+    response = requests.get(url)
+    response.raise_for_status()
+    return response.json()
+
+def find_participant_lists(obj):
+    """
+    Recursively find all lists of strings in a JSON-like object.
+    Returns a list of lists (each list being a potential participant group).
+    """
+    results = []
+
+    if isinstance(obj, dict):
+        for value in obj.values():
+            results.extend(find_participant_lists(value))
+    elif isinstance(obj, list):
+        # Check if this list looks like a participant list (all strings)
+        if all(isinstance(x, str) for x in obj) and len(obj) > 1:
+            results.append(obj)
+        else:
+            for item in obj:
+                results.extend(find_participant_lists(item))
+
+    return results
+
+def build_coattendance_graph(meetings):
+    """Build an undirected co-attendance graph from all detected participant lists."""
+    G = nx.Graph()
+
+    for meeting in meetings:
+        participant_lists = find_participant_lists(meeting)
+        # Merge all string lists found in this record
+        participants = set()
+        for lst in participant_lists:
+            participants.update(lst)
+
+        if len(participants) < 2:
+            continue
+
+        for p in participants:
+            G.add_node(p)
+        for u, v in combinations(participants, 2):
+            if G.has_edge(u, v):
+                G[u][v]['weight'] += 1
+            else:
+                G.add_edge(u, v, weight=1)
+
+    return G
+
+def degree_analysis(G):
+    """Compute degree metrics for the graph."""
+    degree_dict = dict(G.degree())
+    degree_counts = Counter(degree_dict.values())
+    return degree_dict, degree_counts
+
+def write_markdown_report(degree_dict, degree_counts, output_file):
+    """Write results to a Markdown file."""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    with open(output_file, "w", encoding="utf-8") as f:
+        f.write(f"# Degree Analysis Report\n")
+        f.write(f"**Generated on:** {timestamp}\n\n")
+
+        # Summary
+        f.write("## Summary Statistics\n")
+        f.write(f"- Total Nodes: {len(degree_dict)}\n")
+        f.write(f"- Maximum Degree: {max(degree_dict.values()) if degree_dict else 0}\n")
+        f.write(f"- Minimum Degree: {min(degree_dict.values()) if degree_dict else 0}\n\n")
+
+        # Top 10 nodes
+        f.write("## Top 10 Nodes by Degree\n")
+        f.write("| Rank | Node | Degree |\n|------|-------|---------|\n")
+        for i, (node, deg) in enumerate(sorted(degree_dict.items(), key=lambda x: x[1], reverse=True)[:10], 1):
+            f.write(f"| {i} | {node} | {deg} |\n")
+        f.write("\n")
+
+        # Degree distribution
+        f.write("## Degree Distribution\n")
+        f.write("| Degree | Count of Nodes |\n|---------|----------------|\n")
+        for degree, count in sorted(degree_counts.items()):
+            f.write(f"| {degree} | {count} |\n")
+
+    print(f"✅ Markdown report saved to: {output_file}")
+
+def main():
+    url = "https://raw.githubusercontent.com/SingularityNET-Archive/SingularityNET-Archive/refs/heads/main/Data/Snet-Ambassador-Program/Meeting-Summaries/2025/meeting-summaries-array.json"
+    output_file = "degree_analysis_report.md"
+
+    print("📡 Fetching data from remote source...")
+    data = load_json_remote(url)
+    print(f"✅ Downloaded {len(data)} meeting records.")
+
+    print("🔍 Detecting participant lists recursively...")
+    G = build_coattendance_graph(data)
+
+    if len(G.nodes) == 0:
+        print("⚠️ No participant lists found — please check JSON structure manually.")
+        return
+
+    print(f"📊 Built graph with {len(G.nodes)} nodes and {len(G.edges)} edges.")
+
+    degree_dict, degree_counts = degree_analysis(G)
+    write_markdown_report(degree_dict, degree_counts, output_file)
+
+if __name__ == "__main__":
+    main()
+