Betweenness centrality measures how often a node lies on shortest paths between other nodes. A high score identifies a bridge between communities and a potential gatekeeper of information flow. section 5.1 section 8.1.2
Use the measure to interpret network structure and implement it in a projected Neo4j graph:
- Nodes connecting otherwise separate communities receive higher ranks because more shortest paths pass through them. section 8.1.2
- Removing a high-betweenness node can split a network into separate components, revealing its structural importance. section 5.1 section 8.1.2
- Project the follower network as an in-memory graph, retain the previously calculated node features, and run the algorithm in mutate mode. section 8.1.2
Running the algorithm
With the projected graph named knnExample, execute CALL gds.betweenness.mutate('knnExample', {mutateProperty:'betweenness'}). The result stores each node's score in the projected graph under betweenness.
