Overview#
A financial crimes investigator reviewing a suspected money laundering network can see three names connected to the same shell company. What they cannot see without analytical tools is that those three names share a fourth associate through a different corporate structure two jurisdictions away, that the fourth associate's phone number appeared in a completely separate narcotics case six months ago, and that the entire network clusters around two key brokers whose removal would fragment the operation. Argus Link Analysis makes those non-obvious connections visible, turning disconnected data points into the network map that changes how an investigation proceeds.
The platform transforms relationship data from investigations, financial records, communications, and intelligence feeds into actionable network intelligence through advanced visualisation, centrality analysis, community detection, and automated pattern recognition.
Key Features#
Network Visualisation#
- Large-scale relationship mapping across people, organisations, accounts, locations, and events
- Real-time network updates from streaming data sources with instant graph visualisation
- Interactive 3D visualisation with force-directed layouts, temporal animations, and multi-layer views supporting large node counts
- Configurable node and edge styling with entity-type icons, relationship labels, and attribute display
- Geographic overlay mapping network relationships onto spatial coordinates
Centrality and Influence Analysis#
- Advanced centrality analysis covering PageRank, betweenness, closeness, and eigenvector centrality for key player identification
- Influence propagation modelling showing how information or resources flow through networks
- Broker and gatekeeper identification finding entities that control connections between network clusters
- Vulnerability analysis identifying network dependencies and single points of failure
- Network resilience analysis identifying critical nodes whose removal would most disrupt operations
Community and Pattern Detection#
- Community detection using Louvain, label propagation, and hierarchical clustering algorithms to reveal hidden subgroups
- Specialised criminal network analysis algorithms for organised crime, terrorist cells, fraud rings, and money laundering
- Automated pattern recognition identifying suspicious network structures and relationship anomalies
- Structural comparison identifying networks that share similar organisational patterns
- Outlier detection finding entities whose network position is unusual relative to their peers
Temporal and Predictive Analysis#
- Link prediction using machine learning models to forecast missing or hidden relationships
- Temporal network analysis tracking relationship evolution, network growth, and connection patterns over time
- Shortest path analysis finding connections between subjects across multiple degrees of separation
- Network comparison showing structural changes between time periods
- Event-driven network analysis showing how networks reconfigure in response to arrests, seizures, or other disruptions
- Cross-case network comparison identifying structural similarities between criminal organisations
Use Cases#
Criminal Network Mapping. Visualise the complete structure of criminal organisations by mapping relationships between members, identifying leadership through centrality analysis, and discovering communication patterns that reveal operational cells. Track how networks reorganise after enforcement actions.
Financial Fraud Ring Detection. Apply community detection algorithms to financial transaction networks to identify clusters of accounts and entities involved in coordinated fraud, money laundering, or identity theft operations.
Terrorism Network Analysis. Map relationships between suspects, locations, communications, and financial transactions to identify terrorist cells, support networks, and operational planning patterns. Discover peripheral associates through link prediction.
Intelligence Fusion. Combine relationship data from multiple intelligence sources into unified network views, automatically discovering connections between subjects that span different investigations and data sources.
Integration#
- Connects with investigation and case management systems for seamless analysis workflow
- Integrates with financial transaction monitoring platforms for relationship discovery
- Links to intelligence databases and watchlist systems for entity enrichment
- Works with alert systems for automated notification of significant network changes
- Supports export of network visualisations and analysis results for reporting and prosecution
- Compatible with geospatial analysis platforms for location-aware network mapping
- Feeds into analytical dashboards for organisational network intelligence oversight
- Connects with telephone and communication analysis platforms for call record integration
Open Standards#
- STIX 2.1 (OASIS): Threat intelligence about network entities, attack patterns, threat actors, and relationships is ingested and exported as STIX 2.1 Structured Threat Information Expression bundles, enabling interoperability with external intelligence platforms.
- TAXII 2.1 (OASIS): Automated polling of external threat intelligence feeds is performed over the TAXII 2.1 protocol, allowing the graph engine to consume live indicator data from TAXII-compliant sharing servers.
- GEXF 1.3 (Graph Exchange XML Format): Network graphs are exported in GEXF 1.3 format, enabling analysts to load relationship data directly into tools such as Gephi for further visualisation and community detection outside the platform.
- GraphQL (GraphQL Foundation): All graph queries, mutations, and real-time network updates are exposed through a GraphQL API, including typed subscriptions that stream live graph changes to connected clients.
- openCypher: Graph traversal, shortest-path queries, and community detection algorithms execute against a Neo4j property graph store using the openCypher query language.
- GeoJSON (RFC 7946): Geographic coordinates attached to network nodes are expressed as GeoJSON, supporting the geographic overlay views that map relationship networks onto spatial coordinates.
- ISO 8601: All temporal edge and node properties, including relationship start dates, merge timestamps, and event timestamps used in temporal network analysis, are serialised in ISO 8601 format.
Last Reviewed: 2026-02-05 Last Updated: 2026-04-14