Graph Metrics¶

Compute statistics that describe the overall graph structure.

Basic Metrics¶

import grafeo

db = grafeo.GrafeoDB()

# Basic counts via database methods
print(f"Nodes: {db.node_count()}")
print(f"Edges: {db.edge_count()}")

# Additional metrics via algorithms
algs = db.algorithms()

Transitivity (Clustering Coefficient)¶

Global measure of how clustered the graph is.

algs = db.algorithms()
transitivity = algs.transitivity()
print(f"Transitivity: {transitivity:.4f}")

Triangle Count¶

Count triangles for clustering analysis.

algs = db.algorithms()
triangles = algs.triangles()
print(f"Total triangles: {triangles}")

Degree Distribution¶

Use the NetworkX adapter for degree statistics:

nx_adapter = db.as_networkx(directed=True)
dist = nx_adapter.degree_distribution()

for degree, count in sorted(dist.items()):
    print(f"Degree {degree}: {count} nodes")

Summary Table¶

Metric	Range	Interpretation
Density	0-1	Higher = more connected
Transitivity	0-1	Higher = more clustered
Avg Degree	0-n	Higher = more edges per node