[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzk4ODM","title","Graph Theory with AI Applications VOL-2","state","published","contactAuthorMethod","email","aboutText","

Graph Theory with AI Applications: Foundations, Algorithms, and Modern Neural Approaches (VOL-2) continues the journey from classical graph theory into the rapidly evolving world of Graph Artificial Intelligence (Graph AI). While Volume I established the mathematical foundations, graph algorithms, social network analysis, and graph mining techniques, this volume focuses on modern graph representation learning, Graph Neural Networks (GNNs), graph embeddings, explainable graph AI, and emerging research frontiers.

As modern data increasingly takes the form of interconnected networks rather than traditional tabular structures, graph-based machine learning has emerged as one of the most important fields in artificial intelligence. Social networks, recommendation systems, knowledge graphs, cybersecurity infrastructures, molecular structures, financial transaction networks, and autonomous systems all generate graph-structured data that requires specialized learning techniques.

This book provides a comprehensive exploration of Graph Neural Networks, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), GraphSAGE, Message Passing Neural Networks (MPNNs), Relational GNNs, Temporal GNNs, and large-scale industrial graph learning systems.

Readers will learn how graph embeddings such as DeepWalk, Node2Vec, LINE, and Graph Autoencoders enable machines to learn meaningful representations from complex networks. The book further demonstrates how these techniques power recommendation engines, social network analysis, cybersecurity systems, knowledge graphs, natural language processing applications, robotics, and intelligent decision-making systems.

A distinguishing feature of this volume is its strong emphasis on modern AI applications and future research directions. Topics such as explainable graph learning, fairness in graph AI, privacy-preserving graph analytics, distributed graph processing, graph foundation models, neural-symbolic graph reasoning, and quantum graph neural networks are presented in a structured and accessible manner.

Designed for students, researchers, educators, and industry professionals, this book serves as both an academic textbook and a practical reference guide for anyone seeking expertise in graph-based artificial intelligence and modern network learning systems.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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