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"""Compute cluster statistics for all (network, method) pairs + ground truth."""
import argparse
import sys
import os
import json
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import numpy as np
import pandas as pd
import igraph as ig
from config import NETWORKS, METHODS, RESULTS_DIR
from load_data import load_edge_list, load_communities
def build_neighbors(edge_df):
"""Build adjacency dict from edge DataFrame."""
neighbors = {}
for _, row in edge_df.iterrows():
s, t = row["src"], row["tgt"]
neighbors.setdefault(s, set()).add(t)
neighbors.setdefault(t, set()).add(s)
return neighbors
def compute_cluster_stats(network_name, method_name, com_path):
"""Compute statistics for a given community assignment."""
net = NETWORKS[network_name]
edge_df = load_edge_list(net["edge_tsv"])
neighbors = build_neighbors(edge_df)
all_nodes = set(neighbors.keys())
node2com = load_communities(com_path)
# Build com2nodes
com2nodes = {}
for node, com in node2com.items():
com2nodes.setdefault(com, set()).add(node)
# Separate singletons vs non-singletons
non_singleton_coms = {c: nodes for c, nodes in com2nodes.items() if len(nodes) > 1}
singleton_coms = {c: nodes for c, nodes in com2nodes.items() if len(nodes) == 1}
# Nodes in non-singleton clusters
nodes_in_clusters = set()
for nodes in non_singleton_coms.values():
nodes_in_clusters.update(nodes)
node_coverage = len(nodes_in_clusters) / len(all_nodes) if all_nodes else 0.0
# Per-cluster stats
cluster_details = []
total_clusters = len(non_singleton_coms)
for ci, (com_id, nodes) in enumerate(sorted(non_singleton_coms.items())):
n = len(nodes)
# Internal edges
m_internal = 0
c_boundary = 0
for node in nodes:
for nbr in neighbors.get(node, set()):
if nbr in nodes:
m_internal += 1
else:
c_boundary += 1
m_internal //= 2 # each edge counted twice
edge_density = 2 * m_internal / (n * (n - 1)) if n > 1 else 0.0
degree_density = m_internal / n if n > 0 else 0.0
conductance = c_boundary / (2 * m_internal + c_boundary) if (2 * m_internal + c_boundary) > 0 else 0.0
# Minimum edge cut via igraph
mincut = 0
if n >= 2 and m_internal >= 1:
node_list = sorted(nodes)
local_map = {nd: i for i, nd in enumerate(node_list)}
edges = []
for nd in node_list:
for nbr in neighbors.get(nd, set()):
if nbr in nodes and local_map[nd] < local_map[nbr]:
edges.append((local_map[nd], local_map[nbr]))
sg = ig.Graph(n=n, edges=edges, directed=False)
mincut = sg.mincut().value
mincut_over_log10n = mincut / np.log10(n) if n > 1 else 0.0
cluster_details.append({
"com_id": com_id,
"n": n,
"m_internal": m_internal,
"c_boundary": c_boundary,
"edge_density": edge_density,
"degree_density": degree_density,
"conductance": conductance,
"mincut": int(mincut),
"mincut_over_log10n": mincut_over_log10n,
})
if (ci + 1) % 500 == 0:
print(f" ... {ci+1}/{total_clusters} clusters processed")
# Per-node mixing parameter
mixing_params = []
for node in all_nodes:
if node not in node2com:
mixing_params.append(1.0) # outlier
continue
my_com = node2com[node]
nbrs = neighbors.get(node, set())
if len(nbrs) == 0:
mixing_params.append(0.0)
continue
n_in = sum(1 for nbr in nbrs if node2com.get(nbr) == my_com)
mixing_params.append(1.0 - n_in / len(nbrs))
summary = {
"network": network_name,
"method": method_name,
"n_nodes": len(all_nodes),
"n_clusters_total": len(com2nodes),
"n_clusters_non_singleton": len(non_singleton_coms),
"n_singleton_clusters": len(singleton_coms),
"node_coverage": node_coverage,
"mean_mixing_param": np.mean(mixing_params),
"median_mixing_param": np.median(mixing_params),
"mean_cluster_size": np.mean([d["n"] for d in cluster_details]) if cluster_details else 0,
"median_cluster_size": np.median([d["n"] for d in cluster_details]) if cluster_details else 0,
"mean_edge_density": np.mean([d["edge_density"] for d in cluster_details]) if cluster_details else 0,
"median_edge_density": np.median([d["edge_density"] for d in cluster_details]) if cluster_details else 0,
"mean_conductance": np.mean([d["conductance"] for d in cluster_details]) if cluster_details else 0,
"mean_degree_density": np.mean([d["degree_density"] for d in cluster_details]) if cluster_details else 0,
"mean_mincut": np.mean([d["mincut"] for d in cluster_details]) if cluster_details else 0,
"median_mincut": np.median([d["mincut"] for d in cluster_details]) if cluster_details else 0,
"mean_mincut_over_log10n": np.mean([d["mincut_over_log10n"] for d in cluster_details]) if cluster_details else 0,
"n_connected": sum(1 for d in cluster_details if d["mincut"] > 0),
"n_disconnected": sum(1 for d in cluster_details if d["mincut"] == 0),
"n_wellconnected": sum(1 for d in cluster_details if d["mincut"] > np.log10(d["n"])),
}
return summary, cluster_details, mixing_params
def compute_all_stats():
"""Compute stats for all methods on all networks plus ground truth."""
all_summaries = []
stats_dir = os.path.join(RESULTS_DIR, "stats")
os.makedirs(stats_dir, exist_ok=True)
for net_name in NETWORKS:
net = NETWORKS[net_name]
# Ground truth
print(f"Computing stats: {net_name} / ground_truth")
summary, details, mixing = compute_cluster_stats(
net_name, "ground_truth", net["com_gt_tsv"]
)
all_summaries.append(summary)
_save_details(stats_dir, net_name, "ground_truth", details, mixing)
# Each method
for method in METHODS:
m_name = method["name"]
est_path = os.path.join(RESULTS_DIR, net_name, m_name, "com.tsv")
if not os.path.exists(est_path):
print(f" WARNING: {est_path} not found, skipping")
continue
print(f"Computing stats: {net_name} / {m_name}")
summary, details, mixing = compute_cluster_stats(
net_name, m_name, est_path
)
all_summaries.append(summary)
_save_details(stats_dir, net_name, m_name, details, mixing)
df = pd.DataFrame(all_summaries)
out_path = os.path.join(stats_dir, "cluster_stats_summary.csv")
df.to_csv(out_path, index=False)
print(f"\nCluster stats saved to {out_path}")
print(df.to_string(index=False))
return df
def _save_details(stats_dir, net_name, method_name, details, mixing):
"""Save per-cluster details and mixing params as JSON."""
out_dir = os.path.join(stats_dir, net_name, method_name)
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "cluster_details.json"), "w") as f:
json.dump(details, f, indent=2)
with open(os.path.join(out_dir, "mixing_params.json"), "w") as f:
json.dump(mixing, f)
if __name__ == "__main__":
compute_all_stats()
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