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Centrality Algorithms

Centrality algorithms identify the most important nodes in a graph.

Coming Soon

These algorithms are planned for upcoming releases.

PageRank

Measures node importance based on incoming links.

from grafeo.algorithms import pagerank

scores = pagerank(db,
    damping=0.85,
    iterations=20
)

Parameters

Parameter Default Description
damping 0.85 Probability of following a link
iterations 20 Maximum iterations
tolerance 1e-6 Convergence threshold

Use Cases

  • Search engine ranking
  • Social influence analysis
  • Citation importance

Betweenness Centrality

Measures how often a node lies on shortest paths.

from grafeo.algorithms import betweenness_centrality

scores = betweenness_centrality(db)

Use Cases

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

Closeness Centrality

Measures average distance to all other nodes.

from grafeo.algorithms import closeness_centrality

scores = closeness_centrality(db)

Use Cases

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

Degree Centrality

Simple count of connections.

from grafeo.algorithms import degree_centrality

scores = degree_centrality(db,
    direction='both'  # 'in', 'out', or 'both'
)

Use Cases

  • Quick importance estimate
  • Hub identification
  • Activity analysis

Eigenvector Centrality

Importance based on neighbor importance.

from grafeo.algorithms import eigenvector_centrality

scores = eigenvector_centrality(db)

Use Cases

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