{ "item_title" : "Graph Machine Learning", "item_author" : [" Chuan Shi", "Cheng Yang", "Xiao Wang "], "item_description" : "Across fifteen chapters, the book moves from fundamental graph concepts to advanced Graph Neural Network (GNN) architectures, trustworthy graph learning, spectral methods, heterogeneous graphs, and emerging graph foundation models. It not only explains the design logic behind modern graph learning algorithms but also reveals why certain models succeed--or fail--across real world tasks. Readers will explore practical scenarios in social recommendation, financial risk control, and scientific intelligence, gaining the ability to translate theory into effective solutions. The book also highlights frontier directions such as dynamic graphs, hypergraphs, large scale graph learning, and multimodal integration. This book is ideal for university students, engineers, and technical professionals seeking a rigorous yet accessible entry point into graph machine learning. With its combination of conceptual frameworks, platform tools, code implementations, and application case studies, it equips readers to build, optimize, and deploy graph models with confidence--requiring only basic machine learning literacy as a starting point. ", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/81/925/889/9819258898_b.jpg", "price_data" : { "retail_price" : "64.99", "online_price" : "64.99", "our_price" : "64.99", "club_price" : "64.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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