EC-SBM community detection analysis: full pipeline and writeup

Implement community detection on 3 EC-SBM networks (polblogs, topology,
internet_as) using 5 methods (Leiden-Mod, Leiden-CPM at 0.1 and 0.01,
Infomap, graph-tool SBM). Compute AMI/ARI/NMI accuracy, cluster statistics,
and generate figures and LaTeX report.

1 files changed, 47 insertions, 0 deletions

diff --git a/scripts/run_graphtool_sbm.py b/scripts/run_graphtool_sbm.py
new file mode 100644
index 0000000..f860e89
--- /dev/null
+++ b/scripts/run_graphtool_sbm.py
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+"""Run graph-tool SBM inference for community detection."""
+
+import argparse
+import sys
+import os
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import graph_tool.all as gt
+import numpy as np
+
+from config import NETWORKS, RESULTS_DIR
+from load_data import load_edge_list, build_graphtool_graph, save_communities
+
+
+def run_graphtool_sbm(network_name):
+    net = NETWORKS[network_name]
+    edge_df = load_edge_list(net["edge_tsv"])
+    g, name_to_idx, idx_to_name = build_graphtool_graph(edge_df)
+
+    print(f"  Graph: {g.num_vertices()} nodes, {g.num_edges()} edges")
+
+    # Use minimize_blockmodel_dl for flat (non-nested) SBM
+    np.random.seed(42)
+    gt.seed_rng(42)
+    state = gt.minimize_blockmodel_dl(g)
+
+    # Extract block assignments
+    blocks = state.get_blocks()
+    n_blocks = len(set(blocks.a))
+    print(f"  Found {n_blocks} blocks")
+
+    node2com = {}
+    for v in g.vertices():
+        node2com[idx_to_name[int(v)]] = str(blocks[v])
+
+    out_path = os.path.join(RESULTS_DIR, network_name, "graphtool_sbm", "com.tsv")
+    save_communities(node2com, out_path)
+    print(f"  Saved to {out_path}")
+    return node2com
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--network", required=True)
+    args = parser.parse_args()
+    print(f"Running graph-tool SBM on {args.network}...")
+    run_graphtool_sbm(args.network)