{"product_id":"9783031885372","title":"Graph Neural Networks: Essentials and Use Cases","description":"\u003ch1\u003eGraph Neural Networks: Essentials and Use Cases\u003c\/h1\u003e \u003ch2\u003eChelliah, Pethuru Raj; Whig, Pawan; Nagarajan, Susila; Sakthivel, Usha; Yathiraju, Nikhitha\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp data-start=\"0\" data-end=\"513\"\u003eThis book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals—and how they complement advances in generative, agentic, and physical AI—this book is an essential resource for understanding and leveraging their potential.\u003c\/p\u003e\n\u003cp data-start=\"0\" data-end=\"513\"\u003eThe neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation.\u003c\/p\u003e\n\u003cp data-start=\"515\" data-end=\"831\"\u003eTraditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data—complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains.\u003c\/p\u003e\n\u003cp data-start=\"833\" data-end=\"1027\"\u003eGNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged:\u003c\/p\u003e\n\u003cul data-start=\"1029\" data-end=\"1550\"\u003e\n\u003cli data-start=\"1029\" data-end=\"1224\"\u003e\n\u003cstrong data-start=\"1031\" data-end=\"1071\"\u003eGraph Convolutional Networks (GCNs):\u003c\/strong\u003e These networks learn from a node’s local neighborhood by aggregating information from adjacent nodes, updating the node’s representation in the process.\u003c\/li\u003e\n\u003cli data-start=\"1225\" data-end=\"1395\"\u003e\n\u003cstrong data-start=\"1227\" data-end=\"1265\"\u003eGraph Attentional Networks (GATs):\u003c\/strong\u003e By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.\u003c\/li\u003e\n\u003cli data-start=\"1396\" data-end=\"1550\"\u003e\n\u003cstrong data-start=\"1398\" data-end=\"1434\"\u003eGraph Recurrent Networks (GRNs):\u003c\/strong\u003e These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp data-start=\"1552\" data-end=\"1941\"\u003eGNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2025-07-26\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783031885372\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-031-88538-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 418\u003c\/p\u003e ","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":44317955522700,"sku":"9783031885372","price":179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783031885372_16df03f9-4dd7-4894-8fe4-2395a36e2d31.jpg?v=1775065870","url":"https:\/\/lateknightbooks.com\/products\/9783031885372","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}