Nyhet

Complete Guide to Graph Representation Learning with Case Studies

AvE. Chandra Blessie,Pethuru Raj Chelliah

Inbunden, Engelska, 2026

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Beskrivning

Comprehensive resource on graph representation learning (GRL), exploring fundamental principles, advanced methodologies, and case studies A Complete Guide to Graph Representation Learning with Case Studies 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. The 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. The book also includes information on: Node-level features such as node degree, node centrality, closeness, betweenness, eigenvector, page rank centrality, clustering coefficient, closed triangles, egograph, and motifsNeighborhood sampling techniques such as breadth-first sampling, depth-first sampling, snowball sampling, random walk, shallow walk, edge sampling, link-based sampling, and metapath-based samplingDeep learning models including Graph Autoencoder (GAE), Variational Graph Encoder (VGAE), and Graph Attention Network (GAN)Graph alignment and matching, covering subgraph matching and embedding for matchingA Complete Guide to Graph Representation Learning with Case Studies 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.

Produktinformation

Utgivningsdatum:2026-08-20
Format:Inbunden
Språk:Engelska
Antal sidor:448
Förlag:John Wiley & Sons Inc
ISBN:9781394314843

Utforska kategorier

Systemvetenskap och AI inom Data och IT

Mer om författaren

E. CHANDRA BLESSIE, PhD, is Dean of Innovation with the School of Innovation at the KG College of Arts and Science Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India. PETHURU RAJ CHELLIAH, PhD, is Vice President and Chief Architect of the Edge AI Division of Reliance Jio Platforms Ltd. in Bangalore, India. B. SUNDARAVADIVAZHAGAN, PhD, is a Professor with the College of Computing and Information Sciences at the University of Technology and Applied Sciences Al Mussanah, Oman.

Innehållsförteckning

List of Figures xxiList of Tables xxviiAbout the Book xxixAbout the Authors xxxiPreface xxxiiiList of Abbreviation xxxvPart I Foundation Learning 11 Introduction to Graph and Graph Representation Learning 31.1 Introduction 31.2 What Is a Graph? 31.3 Importance of Graph 51.4 Types of Graphs 61.5 Overview of Graph Representation Learning (GRL) 131.6 Overview of Graph Connectivity 151.7 Foundation on Graph Neighborhood 171.8 Applications of Graph 231.9 Case Studies of GRL 251.10 Conclusion 312 Fundamental Concepts of Graph Structure 332.1 Introduction to Graph Structures 332.2 Node-Level Features 352.3 Structural-Level Features 46Contents vii2.4 Graph-Level Features 522.5 Graph-Based Representation Techniques 562.6 Graph Representation Matrix 662.7 Conclusion 723 Overlapping Neighborhood in Graph 733.1 Definition and Its Importance 733.2 Methods for Detecting Neighborhood Overlap 753.3 Neighborhood Overlap Visualization Techniques 813.4 Overlapping Neighborhoods in Heterogeneous Graphs 843.5 Case Studies on Neighborhood Overlap Detection in Real-World Scenarios 923.6 Conclusion 95Part II Core Graph Representation Learning 974 Graph Machine Learning 994.1 Introduction to GML 99Contents ix4.2 Types of Tasks in GML 1014.3 Conclusion 1215 Graph Sampling 1235.1 Introduction 1235.2 Types of Graph Sampling 1255.3 Case Studies 1495.4 Conclusion 1526 Graph Pooling 1536.1 Introduction to Graph Pooling 153x Contents6.2 Types of Graph Pooling 1556.3 Case Study on Graph Pooling 1786.4 Conclusion 1797 Graph Neural Networks and Deep Representation Learning 1817.1 Foundation of Deep Representation Learning on Graphs 1817.2 Introduction to GNNs 1857.3 Fundamentals of GCNs 1897.4 Types of GNNs 1947.5 Applications of Deep Learning on Graphs 2077.6 Conclusion 2088 Advanced Graph Neural Networks 2118.1 Spatiotemporal GNNs 2118.2 Dynamic GNNs 2168.3 Hypergraph Neural Networks 2238.4 Unsupervised Deep Learning Models 2248.5 Conclusion 231Part III Advanced Analysis and Techniques 2339 Graph Alignment and Matching 2359.1 Introduction to Graph Alignment and Matching 2359.2 Definition and Purpose of Graph Alignment and Matching 2359.3 Example for Graph Matching and Alignment 2369.4 Types of Graph Alignment and Matching 2379.5 Mathematical Approaches for Graph Alignment and Matching 2419.6 Case Studies on Graph Alignment and Matching 2469.7 Conclusion 24710 Neighborhood Reconstruction Methods 24910.1 Introduction 24910.2 Neighborhood Reconstruction Techniques 25110.3 Neighborhood Reconstruction Methods 26210.4 Applications of Encoder–Decoder in Graph Learning 26510.5 Case Study on Neighborhood Reconstruction 26610.6 Conclusion 26711 Transfer Learning on Graph 26911.1 Introduction 26911.2 Overview of TGL 27011.3 Types of TGL 27311.4 Key Techniques in TGL 28411.5 Technique Comparisons 29611.6 Case Studies 29711.7 Conclusion 299Part IV Emerging Trends with Case Studies 30112 Graph Contrastive Learning 30312.1 Introduction to Self-Supervised Learning 30312.2 Comparison with Supervised Graph Learning 31112.3 Fundamentals of GCL 31212.4 GCL Frameworks 31612.5 Conclusion 31813 Multimodal Graph Representation Learning 32113.1 Introduction to Multimodal GRL 32113.2 Types of Modalities in Graph Data 32213.3 Data Fusion Techniques in Multimodal GRL 32513.4 Applications 33113.5 Conclusion 33414 Demystifying Graph Embeddings and Industrial Applications 33714.1 Introduction 33714.2 The Importance of Graph Representation 33714.3 Making Sense Out of Graph-Structured Data 33814.4 Delineating AI Model Engineering Steps 33914.5 Graph Embeddings 34014.6 Edge Embedding Techniques 34814.7 Applications of Graph Embeddings 35414.8 Conclusion 35615 Knowledge Graph Foundation, Techniques, and Its Case Studies 35915.1 Introduction to KG 35915.2 Construction of a KG 36515.3 Techniques for Building a KG 36615.4 Representation and Storage 371Contents xvii15.5 Case Studies of KG 37415.6 Conclusion 38316 Graph Representation Learning in Wireless Communication and Tourist Movement Analysis 38516.1 A Graph-Theoretic Framework for Analyzing Tourist Flows from Social Media Data 38516.2 Knowledge-Driven Graph Learning for Next-Generation Wireless Networks 39016.3 Applications of GRL in Wireless Networks and Urban Tourism 39516.4 Other Areas of GRL Applications 39716.5 Conclusion 401References 401Index 403