Graph-powered link analysis, combined with natural language processing (NLP), offers a powerful approach to identifying complex patterns and trends within extensive and complex datasets, especially unstructured data like emails, documents, and social media. By modeling data as interconnected entities and relationships, link analysis reveals hidden connections and supports various applications such as fraud detection, risk mitigation, and network analysis.

At Text Analytics Forum 2025, Urmi Majumder and Kyle Garcia of Enterprise Knowledge discussed this domain of link analysis and its underlying graph technology using a case study in which EK helped a national agency implement a link analysis solution for suspicious behavior detection. 

Participants in this session were able to:

• Learn the range of applications of a link analysis solution across multiple domains;
• Gain practical knowledge of building a modular, cost-effective, and enterprise-integrable, end-to-end link analysis solution;
• Discover how graph data modeling enables pattern recognition and learn crucial aspects of graph model development, and;
• Learn to design, architect, and implement effective solutions using best practices for scalable linked data analysis.