Renderowana dokumentacja
Ta strona renderuje Markdown i Mermaid modulu bezposrednio z publicznego zrodla dokumentacji.
Overview#
The Graph Centrality Analysis module delivers sophisticated influence metrics that identify key actors, critical infrastructure, and network hubs within complex transaction graphs. With rapid analysis capabilities on large-scale graphs containing millions of nodes, the system deploys six battle-tested centrality algorithms to reveal hidden power structures, chokepoints, and critical investigation targets.
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 exceeding 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 normalized 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 organizational structure within criminal organizations through influence ranking
- Asset Recovery: Target central nodes maximizing recovery success rates through systematic importance analysis
- Financial Crime Intelligence: Detect coordination points and laundering chokepoints within complex financial networks
Integration#
- Connects with the graph analysis engine for centrality computation across investigation graphs
- Compatible with investigation platforms for automated target prioritization
- Supports real-time updates as new graph data arrives
- Export capabilities for centrality results to visualization and reporting tools
- Role-based access controls ensuring secure analysis across teams
- Multi-tenant isolation for organizational data separation
Last Reviewed: 2026-02-23