Network Intelligence

See the Connections That Break Cases Wide Open

Reveal criminal networks hiding in plain sight

Criminal networks hide in plain sight, buried in spreadsheets, scattered across case files, invisible in text reports. Argus Graph & Relationship Analysis renders thousands of entities and relationships instantly, revealing organizational structures that manual analysis would never find.

CJIS Ready10,000+ Nodes at 60fpsReal-Time Collaboration
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The Detective's Dilemma

The murder board has been Detective Maria Reyes's constant companion for three months. Thirty-seven photos. Nineteen suspects. Eight possible witnesses. Four connected addresses. And somewhere in this web of string and pushpins, the key to a serial burglary ring that's hit forty-three homes across three jurisdictions. What Maria doesn't know is that the pattern she's looking for is already there, hidden in complexity beyond human cognitive capacity. Four degrees of separation. Three clicks in a system designed to surface exactly these connections.

Your Whiteboard Can't Scale to Modern Investigations

For decades, investigators have relied on the same basic approach to understanding criminal relationships: association charts drawn on paper, org charts sketched on whiteboards, string connecting photos on murder boards. These methods fail completely when criminal enterprises span dozens of participants, multiple shell companies, hundreds of communications, and years of activity.

A mid-size drug trafficking investigation might involve 200 individuals, 50 businesses, 1,000 phone calls, and connections spanning three states. Drawing this by hand isn't just inefficient, it's impossible. Investigators literally cannot visualize networks of this scale using traditional methods.

When Investigator A draws a network chart and Investigator B draws another, reconciling them requires sitting in the same room and comparing notes. Scale that to a multi-agency task force with twenty analysts across five jurisdictions, and the insight that would break the case never surfaces.

Criminal organizations evolve. Leaders get arrested and lieutenants step up. Cells form and dissolve. A static org chart captures one moment in time. It can't show you how the network arrived at its current structure or predict where it's heading.

What If You Could Actually See Everything at Once?

Walk through an investigation that transforms from a single connection to complete network understanding.

Stage 1 of 5
Entities:2
Relationships:1
Time Elapsed:30 sec
Network GraphA force-directed graph showing 2 entities and their relationships

Initial Connection

A burglary victim reports stolen property appearing at a pawn shop. The investigator adds two entities and one relationship.

Simple. Obvious. The kind of connection anyone could draw on paper. But the system already knows more.

The Technology Behind the Insight

Advanced graph analysis capabilities that extend investigative reach into complexity that overwhelms human cognitive capacity.

10,000+ nodes at 60fps

WebGL-Powered Visualization

Traditional graph tools slow to a crawl beyond a few hundred nodes. Argus leverages WebGL, the same technology powering modern video games, to render massive networks with smooth panning, instant zooming, and fluid interaction.

GPU-accelerated rendering enables investigations that were impossible to visualize to become comprehensible at a glance.

Auto-identify organizational clusters

Intelligent Community Detection

Criminal organizations have structure, hierarchy, cells, specializations, that hides in patterns of relationship and communication. Advanced algorithms automatically identify organizational clusters.

Louvain method for community detection reveals distribution cells, leadership structures, and family versus business associations.

All paths in milliseconds

Interactive Path Analysis

How does the street dealer connect to the cartel supplier? Who bridges rival gang factions? Click two entities and see every path connecting them.

Understand not just that a connection exists, but how information and value flow through the network.

Watch networks evolve

Temporal Network Evolution

Criminal networks aren't static. Members get arrested, new recruits join, leadership changes hands. Track network evolution over time with animated playback.

Temporal views reveal recruitment pipelines, succession planning, and organizational resilience invisible in static analysis.

Identify high-value targets

Network Centrality Analysis

Not all network members are equal. Some control information flow, some bridge disconnected groups, some would fracture the entire organization if removed.

Mathematical centrality metrics quantify each entity's importance, transforming enforcement from volume-based to impact-based.

Multi-user simultaneous editing

Real-Time Collaboration

When multiple investigators work the same network, everyone sees changes instantly. No file sharing, no version control problems.

Annotations, notes, and relationship classifications sync across the team in real time, with complete visibility into who added what.

Use Case Scenarios

See how Graph Analysis transforms different types of investigations.

Drug Trafficking Network

Map distribution networks from street-level dealers to international suppliers

Complexity:200+ entities

Identified 3 distribution cells and leadership structure

Learn More

Financial Fraud Web

Trace shell companies, money flows, and beneficial ownership networks

Complexity:150+ entities

Traced laundering chain through 7 intermediaries

Learn More

Gang Intelligence

Map membership, rivalries, territory, and organizational succession

Complexity:500+ entities

Mapped organizational succession after arrests

Learn More

Cold Case Connection

Connect cross-jurisdictional crimes spanning years of activity

Complexity:300+ entities spanning 10 years

Linked 12 cases to single perpetrator across 4 counties

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Integration Ecosystem

Graph Analysis serves as the analytical core for understanding connections across the entire Argus platform.

Hub
Graph &
Analysis

Click a module to explore integration

What Makes Modern Graph Analysis Different

See how Argus compares to traditional and legacy approaches.

CapabilityLegacy ToolsBasic ToolsArgus
Node CapacityHundredsThousands10,000+ at 60fps
CollaborationFile sharingLimitedReal-time multi-user
DeploymentDesktop onlyDesktopCloud-native anywhere
Algorithm SupportBasicModerateAdvanced (PageRank, Louvain, Dijkstra)
Training RequiredWeeksDaysHours with AI guidance
Court ReadinessVariableLimitedFull provenance tracking

Technical Specifications

Detailed technical information for IT and procurement teams.

Built on Neo4j graph database with query optimization for law enforcement patterns. Scalable architecture supporting millions of entities and relationships. Automatic indexing for common traversal patterns.

PageRank centrality for influence analysis. Louvain community detection for organizational structure. Dijkstra shortest path for connection analysis. Motif pattern detection for recurring structures. Custom algorithms available for specialized analysis needs.

WebGL-based rendering with GPU acceleration. Level-of-detail optimization for smooth performance. 60fps guarantee with 10,000+ nodes. Canvas fallback for unsupported browsers. Adaptive quality based on device capabilities.

GraphML format for interoperability. JSON export for custom integrations. High-resolution PNG/SVG images. Court presentation packages with full provenance. Automated report generation with network metrics.

CJIS-ready architecture with required security controls. FedRAMP-ready deployment options. Complete audit logging of all graph operations. Role-based access control at entity and relationship level. Data encryption at rest and in transit.

Critical Intelligence

The Cost of Connections You Can't See

The intelligence failures that enabled the worst attacks of the past two decades share a common thread: information existed to prevent them, but no one could see how it connected.

The 9/11 hijackers lived with an FBI informant while the CIA held their identities. The connection was there, in different databases, invisible to any single analyst.

The Boston Marathon bombers' leader returned to the US three days after Russian forces killed his known associate. The temporal correlation was there, but no system surfaced it.

The serial killer who terrorized California for twelve years left evidence across ten counties. The DNA connections were there, but jurisdictional fragmentation kept investigators from seeing the pattern.

The tools to see these connections exist. The only question is whether agencies will deploy them.

What Would You See If You Could See Everything?

Your data holds answers you haven't found yet. Patterns you haven't recognized. Connections you haven't seen.