GraphAcademy

Graph Data Science

betweenness centrality

A score for each node equal to how often it lies on the shortest paths between other nodes.

Example

Every route to Eve runs through Dan. Every route between Ava or Cleo and the rest runs through Ben.

cypher
CALL gds.betweenness.stream('people')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS person, score
ORDER BY score DESC

Ben scores 5, lying on the shortest path for five pairs: Ava with Cleo, Dan and Eve, and Cleo with Dan and Eve. Dan scores 3, for Ava, Cleo and Ben each reaching Eve. Ava, Cleo and Eve score 0, because no shortest path passes through an end of the graph.

Lessons that use this term

The lesson and course links below open in a new tab.

9 lessons use this term. Results are ordered by term density.

Analyze Graph Data with Python12 mentions

  • Betweenness CentralityGDS Python ClientCentrality identifies these connectors—nodes that link different parts of the network8 mentions
  • Workshop Complete!Aura Graph Analyticsto native Python projections Analyzed citation networks with PageRank and Betweenness Centrality Detected research communities with Louvain Generated FastRP embeddings for machine learning1 mention
  • Citation NetworksGDS Python ClientRun Betweenness Centrality to find bridge1 mention
  • Introduction to the Python GDS ClientGDS Python ClientProjecting graphs into memory Running algorithms like PageRank, Betweenness Centrality, Louvain, and FastRP Processing results as DataFrames Cleaning up projections when we're1 mention
  • PageRankGDS Python ClientCitation count gives you raw popularity Betweenness Centrality shows bridges between communities (we'll cover this next) Community membership reveals which cluster a paper1 mention

Get started with Graph Data Science8 mentions

  • Challenge: Projection modeling and analysisEssential projection techniquesBetweenness Centrality: Measures bridge positions in the network ([Betweenness centrality2 mentions
  • Algorithms overviewWorking with algorithmsthree most common centrality algorithms are Degree Centrality, PageRank, and Betweenness Centrality5 mentions
  • Understand the five execution modesWorking with algorithmsif estimate shows your graph has 250 million nodes and you're planning to run betweenness centrality (O(n³) complexity), you can anticipate an extremely long runtime—potentially1 mention

Graph Data Science in Practice1 mention

  • Applying AlgorithmsGDS Foundationsgraphs showing degree, PageRank, and betweenness centrality with nodes of different1 mention

Related terms

PageRank
A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are.9 shared lessons
FastRP
A node embedding algorithm, short for Fast Random Projection. It builds each node's vector by combining random vectors drawn from the nodes around it.4 shared lessons
centrality
How important a node is within a graph. Each centrality algorithm defines importance differently.5 shared lessons
modularity
A score for how much more densely connected the nodes within a group are than they would be by chance.3 shared lessons
node embedding
A list of numbers that stands in for a node's position in the graph, so that nodes in similar positions get similar lists.2 shared lessons
Louvain algorithm
A community detection algorithm that repeatedly merges nodes into groups for as long as merging raises modularity.5 shared lessons
Dijkstra's algorithm
An algorithm that finds the cheapest route between two nodes.3 shared lessons
degree centrality
A score for each node equal to its number of outgoing relationships.4 shared lessons

All glossary terms