Graph-powered machine learning

1. Machine learning and graphs : an introduction -- 2. Graph data engineering -- 3. Graphs in machine learning applications -- 4. Content-based recommendations -- 5. Collaborative filtering -- 6. Session-based recommendations -- 7. Context-aware and hybrid recommendations -- 8. Basic approaches to graph-powered fraud detection -- 9. Proximity-based algorithms -- 10. Social metwork analysis against fraud -- 11. Graph-based natural language processing -- 12. Knowledge graphs.

Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendiation systems. "Graph-powered machine learning" teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you'll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.

Includes bibliographical references and index.

1. Machine learning and graphs : an introduction -- 2. Graph data engineering -- 3. Graphs in machine learning applications -- 4. Content-based recommendations -- 5. Collaborative filtering -- 6. Session-based recommendations -- 7. Context-aware and hybrid recommendations -- 8. Basic approaches to graph-powered fraud detection -- 9. Proximity-based algorithms -- 10. Social metwork analysis against fraud -- 11. Graph-based natural language processing -- 12. Knowledge graphs.