Graph Centrality Analysis

Overview#

Two companies appear unconnected in every corporate registry and sanctions database. No shared directors, no common addresses, no overlapping shareholders. Then an investigator runs betweenness centrality on the transaction graph. A single shell company sits precisely at the junction between both firms, acting as the sole relay for dozens of fund transfers. Without centrality analysis, that shell company is invisible. With it, it becomes the obvious starting point for the entire investigation.

The Graph Centrality Analysis module delivers sophisticated influence metrics that identify key actors, critical infrastructure, and network hubs within complex transaction graphs. Six battle-tested centrality algorithms reveal hidden power structures, chokepoints, and high-value investigation targets across graphs built on Neo4j's relationship layer, with PostgreSQL as the authoritative data store.

graph TD
    G[Investigation Graph] --> D[Degree Centrality]
    G --> B[Betweenness Centrality]
    G --> C[Closeness Centrality]
    G --> E[Eigenvector Centrality]
    G --> PR[PageRank]
    G --> K[Katz Centrality]
    D --> R[Ranked Target List]
    B --> R
    C --> R
    E --> R
    PR --> R
    K --> R
    R --> T[Investigation Prioritisation]

Key Features#

• Real-time centrality computation on large-scale graphs with incremental updates for ongoing tracking
• Six centrality algorithms: degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, and Katz centrality
• AI-driven importance ranking for hub detection that exceeds manual analysis accuracy
• Critical node identification pinpointing high-value investigation targets
• Degree centrality measuring direct connections to identify highly connected hubs, exchanges, and coordination points
• Betweenness centrality identifying bridge nodes connecting separate communities and acting as gatekeepers
• Closeness centrality measuring how efficiently a node can reach all others in the network
• Eigenvector centrality scoring importance based on the quality and influence of connected nodes
• Weighted and normalised scoring for cross-network comparisons
• Hub classification from super-hubs through to regular nodes based on connection volume

Use Cases#

• Cryptocurrency Investigation: Identify exchange addresses, mixing services, and money mules through degree and betweenness centrality analysis of transaction networks
• Criminal Network Analysis: Reveal leadership hierarchies and organisational structure within criminal organisations through influence ranking
• Asset Recovery: Target central nodes to maximise recovery success rates through systematic importance analysis
• Financial Crime Intelligence: Detect coordination points and laundering chokepoints within complex financial networks

Integration#

• Connects with the Neo4j graph analysis layer for centrality computation across investigation graphs
• Compatible with investigation platforms for automated target prioritisation
• Supports real-time updates as new graph data arrives from the platform's 153 third-party integrations
• Export capabilities for centrality results to visualization and reporting tools
• Role-based access controls ensuring secure analysis across teams
• Multi-tenant isolation for organisational data separation

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14