Centrality Algorithms¶

Centrality algorithms identify the most important nodes in a graph.

PageRank¶

Measures node importance based on incoming links.

import grafeo

db = grafeo.GrafeoDB()
algs = db.algorithms()

scores = algs.pagerank()

for node_id, score in scores.items():
    print(f"Node {node_id}: {score:.4f}")

Use Cases¶

• Search engine ranking
• Social influence analysis
• Citation importance

Betweenness Centrality¶

Measures how often a node lies on shortest paths.

algs = db.algorithms()
scores = algs.betweenness_centrality()

Use Cases¶

• Identifying bridges/brokers
• Network vulnerability analysis
• Information flow bottlenecks

Closeness Centrality¶

Measures average distance to all other nodes.

algs = db.algorithms()
scores = algs.closeness_centrality()

Use Cases¶

• Identifying well-connected nodes
• Optimal placement problems
• Influence spread analysis

Degree Centrality¶

Simple count of connections.

algs = db.algorithms()
scores = algs.degree_centrality()

Use Cases¶

• Quick importance estimate
• Hub identification
• Activity analysis

Eigenvector Centrality¶

Importance based on neighbor importance.

algs = db.algorithms()
scores = algs.eigenvector_centrality()

Use Cases¶

• Social influence
• Similar to PageRank but undirected
• Prestige measurement