Centrality
Centrality is a family of graph measures that quantify a node’s structural importance or influence according to specified criteria.
Graph Algorithms for Data Science
A centrality measure quantifies the importance or influence of a node according to a specified structural criterion.
- How it is used
- Identifies important or influential nodes in social networks and supports finding influential users.
- How deep it goes
- Defines centrality and contrasts degree centrality with information-flow influence, but does not detail calculation procedures.
- Examples
- Uses a friendship network with three communities and bridge nodes whose removal fragments the network.
- What you can do after
- Evaluate node influence by counting relationships or examining control over information flow.
Read how Graph Algorithms for Data Science explains centrality →
Financial AI in Practice
The value assigned to a node by a centrality measure.
- How it is used
- Uses node importance in fraud graphs to surface influential accounts and bridges in illicit flows.
- How deep it goes
- Names PageRank and betweenness centrality and describes score handling, but provides no calculation or worked score.
- Examples
- Illustrates PageRank with influential fraud-ring or money-laundering accounts and betweenness centrality with bridges in illicit flows.
- What you can do after
- Evaluate/understand which centrality measure could flag influential accounts or bridges, and where computed scores fit in downstream reporting.
Related concepts
- GDS librarymeasured by · The Neo4j Graph Data Science library is a software library providing graph algorithms, projections, and analytical workflows for graph data.
- graph algorithmkind of · A graph algorithm is a computational procedure that operates on graph vertices, edges, and relationships to compute properties or solve problems.
- PageRankincludes · PageRank is a link-analysis algorithm that estimates the relative importance of nodes in a directed graph from the structure and importance of incoming links.
- node importancemeasures · Node importance is a graph property that quantifies the significance of a node according to a chosen criterion such as influence over information flow.
- closeness centralityincludes · Closeness centrality is a graph metric based on the inverse of a node’s shortest-path distances to other reachable nodes.
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