Applied Graph Data Science

Applied Graph Data Science by Pethuru Raj, Pushan Kumar Dutta, Peter Han Joo Chong, Houbing Herbert Song, and Dmitry A. Zaitsev is an essential, hands-on guide for professionals seeking to unlock insights from connected data. This authoritative work draws on leading research and industrial practice to introduce scalable techniques in graph analytics, network science, and graph-based machine learning.

Discover clear, real-world explanations of graph modeling, community detection, centrality measures, link prediction, and graph neural networks (GNNs). The book balances theory with practical application, showing how to translate complex graph algorithms into actionable solutions for fraud detection, recommendation engines, knowledge graphs, cybersecurity, supply-chain optimization, and social network analysis. Readers will appreciate stepwise case studies that map problem statements to deployable strategies and measurable business outcomes.

Designed for data scientists, engineers, analysts, and technical managers across the US, Europe, India, and the Asia-Pacific region, this book equips teams to integrate graph databases, build predictive graph models, and scale analytics in production environments. Emphasis on performance, interpretability, and deployment makes it relevant for enterprises and startups alike.

If you want to transform relational and semi-structured data into competitive advantage, this book provides the guidance to get there. Practical, contemporary, and globally applicable, Applied Graph Data Science is the resource you need to master connected-data challenges and drive measurable results. Add it to your collection and begin building smarter, graph-powered solutions today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.