{"product_id":"9781394314843","title":"A Complete Guide to Graph Representation Learning with Case Studies","description":"\u003ch1\u003eA Complete Guide to Graph Representation Learning with Case Studies\u003c\/h1\u003e\u003ch3\u003eE. Chandra Blessie | Pethuru Raj Chelliah | B. Sundaravadivazhagan\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Data Science \/ Neural Networks\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp\u003e\u003cb\u003eComprehensive resource on graph representation learning (GRL), exploring fundamental principles, advanced methodologies, and case studies\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eA Complete Guide to Graph Representation Learning with Case Studies\u003c\/i\u003e provides a concise understanding of the subject of graph representation learning (GRL), a rapidly advancing field in the domain of machine learning. The book explores basic concepts to state-of-the-art techniques, enabling readers to progress from a fundamental understanding of the approach to mastering its application. The authors also cover the topics of graph embedding methods, graph neural network (GNN) -based approaches, and the latest trends in GRL such as deep learning, transfer learning, graph pooling, alignment, and matching, and graph machine learning. \u003c\/p\u003e\n\u003cp\u003eThe book includes examples of applications of graph learning methods with real-world case studies in which the covered methods can be utilized. It also includes innovative solutions to graph machine learning problems such as node classification, link prediction, and unsupervised learning, and discusses neighborhood overlap visualization techniques and overlapping neighborhoods in heterogeneous graphs. Finally, the book provides an overview of open and ongoing research directions and student projects, providing a glimpse into potential avenues for future work. \u003c\/p\u003e\n\u003cp\u003eThe book also includes information on: \u003c\/p\u003e\n\u003cul\u003e \u003cli\u003eNode-level features such as node degree, node centrality, closeness, betweenness, eigenvector, page rank centrality, clustering coefficient, closed triangles, egograph, and motifs\u003c\/li\u003e \u003cli\u003eNeighborhood sampling techniques such as breadth-first sampling, depth-first sampling, snowball sampling, random walk, shallow walk, edge sampling, link-based sampling, and metapath-based sampling\u003c\/li\u003e \u003cli\u003eDeep learning models including Graph Autoencoder (GAE), Variational Graph Encoder (VGAE), and Graph Attention Network (GAN)\u003c\/li\u003e \u003cli\u003eGraph alignment and matching, covering subgraph matching and embedding for matching\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eA Complete Guide to Graph Representation Learning with Case Studies\u003c\/i\u003e is a thorough and up-to-date reference on the subject for engineers and researchers in data science and machine learning as well as graduate students in related programs of study.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eE. Chandra Blessie,\u003c\/b\u003e PhD, is Dean of Innovation, School of Innovation, KG College of Arts and Science, Coimbatore, Tamil Nadu, India.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePethuru Raj Chelliah,\u003c\/b\u003e PhD, SMIEEE, is the Principal AI Architect at Infocion Inc., AKR Tech Park, Hosur Road, Bangalore, India.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. Sundaravadivazhagan,\u003c\/b\u003e PhD, is a Professor with the College of Computing and Information Sciences at the University of Technology and Applied Sciences Al Mussanah, Oman.\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e24 August 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-IEEE Press\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781394314843\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e464\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44510491541644,"sku":"9781394314843","price":135.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781394314843.jpg?v=1780618327","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781394314843","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}