What is Graph Analytics

Data surrounds us, often forming complex networks that are difficult to interpret. Graph analytics, a subset of link analysis, is essential for unraveling these networks and extracting actionable insights.

Amidst the flood of digital data, graph analytics solutions have become a key capability for law enforcement and intelligence organizations. Graph analytics solutions can help reveal hidden patterns, detect anomalies, and predict future trends, which would be unattainable through manual analysis, thus providing valuable insights that drive decision-making.

Graph analytics for crime and intelligence investigations

The use of graph analytics in law enforcement and intelligence operations is crucial. While an important application is mapping criminal networks, it's also useful for other important tasks, like figuring out crime patterns, managing resources better, and improving investigations.

This visual representation of data can save valuable time in investigations by surfacing key players, revealing communication patterns and discovering hidden relationships within a criminal network. A visual representation of data is typically easier to understand and communicate with than raw data. In this context, nodes are used to represent entities like individuals, mobile devices, companies, organizations or bank accounts, and edges depict their relationships or interactions. By enabling analysts and investigators to work more quickly and effectively, graph analytics helps accelerate investigations and solves cases faster.

Inferred relations

Graph analytics: Use cases and benefits

Using graph analytics provides clear benefits in many types of investigations. It allows investigators to process large amounts of data quickly and efficiently, uncover hidden connections, and make more informed decisions. Examples include:

Drug Trafficking Investigations: In a drug trafficking investigation, nodes could represent suspects, locations and mobile devices, while edges could represent calls made between numbers or movements between locations. By analyzing this visual graph, investigators can identify central figures in the networks, uncover previously unknown connections and discover distribution channels and methods. These valuable insights can be used for prioritizing resources to take down a drug trafficking network.

Financial Crime Investigations: By analyzing transactions, bank records, mobile payments data, cryptocurrency transactions, social media data, and other digital footprints, investigators can map out complex fraud operations, identify shell companies and detect suspicious anomalies in financial transactions. This can shorten the time it takes authorities to investigate financial crimes and neutralize these illicit operations.

Financial graph analytics

The unstructured data challenge in graph analytics

However, graph analytics is not without its challenges. One of the main limitations in traditional graph analytics solutions is dealing with unstructured data, such as audio recordings, images, social media posts or videos. Unlike structured data, which is organized in a predefined manner, unstructured data is more chaotic and harder to analyze.

To overcome this challenge, big data algorithms such as natural language processing and object detection from images can be applied to extract meaningful information from unstructured data. Once the unstructured data is processed and analyzed, it can be visualized and linked to other structured or unstructured data points.

Graph analytics example

Going beyond graph analytics

While graph analytics is a powerful tool, it's not the only one available to investigators. Other capabilities, such as AI-powered enrichment of content, geo-analysis maps and timeline analysis, can be useful and even crucial to complement graph analytics and provide a more comprehensive view of the data.

For example, AI-powered natural language processing can extract key information from a database of scanned reports and provide context to the relationships in the network. Geo-analysis adds a spatial dimension, identifying location patterns or anomalies among network nodes and visualizing it on a map, and timeline analysis contributes a temporal perspective, revealing patterns or anomalies in the timing of events. When combined with graph analytics, which visualizes relationships and interactions, these tools can uncover hidden patterns and connections that significantly accelerate case resolution

The connection to decision intelligence

Decision intelligence platforms can leverage graph analytics alongside AI, machine learning, and data fusion to empower law enforcement with comprehensive insights and strategic advantages, including:

Actionable insights: Decision intelligence systems use insights from graph analytics to determine the best strategies for disrupting the network, such as targeting specific key players or chokepoints within the organization.

Predictive analytics: Predictive models based on graph data help anticipate the potential reactions of the network to law enforcement actions, enabling the agency to plan preemptive measures and allocate resources accordingly.

Automated decision support: Real-time graph analytics data integrated with decision intelligence systems can provide on-the-spot recommendations to officers during operations, such as identifying high-risk individuals or locations that require immediate attention.

Unified data view: By fusing data from various sources, decision intelligence platforms provide a holistic view that includes graph analytics visualizations.

Graph analytics simplifies complex data through visual relationships between entities. Integrated with AI and advanced techniques, it accelerates case investigations and enhances strategic insights, which empowers law enforcement to rapidly uncover and address emerging threats and resolve cases faster.

Learn how Nexyte can accelerate your investigations with graph analytics and decision intelligence.

Public link

Please use the above public link if you want to share this noodl on another website.