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Graph Analytics

Graph Analytics is a technique that models data as networks of nodes (entities) and edges (relationships) to detect complex patterns and insights that traditional analysis often misses.

Overview

Graph Analytics enables analysts to analyze relationships and dependencies between people, organizations and events at scale. It uncovers hidden links across massive, diverse datasets—helping law enforcement and intelligence agencies map criminal activity, fraud rings, and coordinated cyber operations.

What Is Graph Analytics?

Graph Analytics uses mathematical graph theory to analyze connections among data entities. This approach reveals networks, hierarchies, and anomalies that may indicate suspicious activity. Explore Cognyte’s Graph Analytics capabilities to learn how it enhances investigative accuracy.

How Does Graph Analytics Work?

Graph databases store entities and their relationships as nodes and edges, enabling complex queries and visual exploration. Algorithms such as centrality, clustering and pathfinding expose key players and connections, providing actionable insights for operational and investigative decision-making.

Why Use Graph Analytics & Why Is It Important?

Graph Analytics helps uncover suspects, detect suspicious activities, and expose hidden links within massive volumes of unstructured data. It provides a dynamic and visual understanding of relationships that static reports cannot offer.

Cognyte’s Solution for Graph Analytics

Cognyte’s NEXYTE platform integrates advanced Graph Analytics capabilities to visualize multi-layered connections, support intelligence analysis and accelerate investigations. It enables agencies to uncover criminal and terror networks faster and with greater precision.