📮 Graph Learning Techniques by Baoling Shan (.ePUB)+

This comprehensive guide addresses key challenges at the intersection of Data Science, Graph Learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. Graphs serve as a widely recognized representation of the network structure of interconnected data. They appear in various application domains, including social systems, ecosystems, biological networks, knowledge graphs, and information systems. As Artificial Intelligence technologies continue to advance, Graph Learning (i.e., Machine Learning applied to graphs) is attracting increasing interest from both researchers and practitioners. This approach has proven effective for numerous tasks, such as classification, link prediction, and matching, by utilizing Machine Learning algorithms to extract pertinent features from graphs. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.

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