Graph Metrics¶

Compute statistics that describe the overall graph structure.

Basic Metrics¶

from grafeo.algorithms import graph_metrics

metrics = graph_metrics(db)

print(f"Nodes: {metrics.node_count}")
print(f"Edges: {metrics.edge_count}")
print(f"Density: {metrics.density:.4f}")
print(f"Avg Degree: {metrics.average_degree:.2f}")

Density¶

Ratio of actual edges to possible edges.

from grafeo.algorithms import density

d = density(db)
# 0 = no edges, 1 = complete graph

Diameter¶

Longest shortest path in the graph.

from grafeo.algorithms import diameter

d = diameter(db)
print(f"Graph diameter: {d}")

Radius¶

Minimum eccentricity (shortest max distance from any node).

from grafeo.algorithms import radius

r = radius(db)

Clustering Coefficient¶

Measure of how clustered the graph is.

from grafeo.algorithms import clustering_coefficient

# Global clustering coefficient
global_cc = clustering_coefficient(db, method='global')

# Local clustering coefficients
local_cc = clustering_coefficient(db, method='local')

# Average local
avg_cc = clustering_coefficient(db, method='average')

Degree Distribution¶

from grafeo.algorithms import degree_distribution

dist = degree_distribution(db)

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

Summary Table¶

Metric	Range	Interpretation
Density	0-1	Higher = more connected
Diameter	1-n	Lower = more compact
Clustering	0-1	Higher = more clustered
Avg Degree	0-n	Higher = more edges per node