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Produktbild: A Complete Guide to Graph Representation Learning with Case Studies

A Complete Guide to Graph Representation Learning with Case Studies

143,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.08.2026

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

23,4/16/3 cm

Gewicht

748 g

Sprache

Englisch

ISBN

978-1-394-31484-3

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

24.08.2026

Verlag

John Wiley & Sons

Seitenzahl

448

Maße (L/B/H)

23,4/16/3 cm

Gewicht

748 g

Sprache

Englisch

ISBN

978-1-394-31484-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: A Complete Guide to Graph Representation Learning with Case Studies

  • List of Figures xxi
    List of Tables xxvii
    About the Book xxix
    About the Authors xxxi
    Preface xxxiii
    List of Abbreviation xxxv

    Part I Foundation Learning 1

    1 Introduction to Graph and Graph Representation Learning 3
    1.1 Introduction 3
    1.2 What Is a Graph? 3
    1.3 Importance of Graph 5
    1.4 Types of Graphs 6
    1.5 Overview of Graph Representation Learning (GRL) 13
    1.6 Overview of Graph Connectivity 15
    1.7 Foundation on Graph Neighborhood 17
    1.8 Applications of Graph 23
    1.9 Case Studies of GRL 25
    1.10 Conclusion 31

    2 Fundamental Concepts of Graph Structure 33
    2.1 Introduction to Graph Structures 33
    2.2 Node-Level Features 35
    2.3 Structural-Level Features 46Contents vii
    2.4 Graph-Level Features 52
    2.5 Graph-Based Representation Techniques 56
    2.6 Graph Representation Matrix 66
    2.7 Conclusion 72

    3 Overlapping Neighborhood in Graph 73
    3.1 Definition and Its Importance 73
    3.2 Methods for Detecting Neighborhood Overlap 75
    3.3 Neighborhood Overlap Visualization Techniques 81
    3.4 Overlapping Neighborhoods in Heterogeneous Graphs 84
    3.5 Case Studies on Neighborhood Overlap Detection in Real-World Scenarios 92
    3.6 Conclusion 95

    Part II Core Graph Representation Learning 97

    4 Graph Machine Learning 99
    4.1 Introduction to GML 99Contents ix
    4.2 Types of Tasks in GML 101
    4.3 Conclusion 121

    5 Graph Sampling 123
    5.1 Introduction 123
    5.2 Types of Graph Sampling 125
    5.3 Case Studies 149
    5.4 Conclusion 152

    6 Graph Pooling 153
    6.1 Introduction to Graph Pooling 153x Contents
    6.2 Types of Graph Pooling 155
    6.3 Case Study on Graph Pooling 178
    6.4 Conclusion 179

    7 Graph Neural Networks and Deep Representation Learning 181
    7.1 Foundation of Deep Representation Learning on Graphs 181
    7.2 Introduction to GNNs 185
    7.3 Fundamentals of GCNs 189
    7.4 Types of GNNs 194
    7.5 Applications of Deep Learning on Graphs 207
    7.6 Conclusion 208

    8 Advanced Graph Neural Networks 211
    8.1 Spatiotemporal GNNs 211
    8.2 Dynamic GNNs 216
    8.3 Hypergraph Neural Networks 223
    8.4 Unsupervised Deep Learning Models 224
    8.5 Conclusion 231

    Part III Advanced Analysis and Techniques 233

    9 Graph Alignment and Matching 235
    9.1 Introduction to Graph Alignment and Matching 235
    9.2 Definition and Purpose of Graph Alignment and Matching 235
    9.3 Example for Graph Matching and Alignment 236
    9.4 Types of Graph Alignment and Matching 237
    9.5 Mathematical Approaches for Graph Alignment and Matching 241
    9.6 Case Studies on Graph Alignment and Matching 246
    9.7 Conclusion 247

    10 Neighborhood Reconstruction Methods 249
    10.1 Introduction 249
    10.2 Neighborhood Reconstruction Techniques 251
    10.3 Neighborhood Reconstruction Methods 262
    10.4 Applications of Encoder-Decoder in Graph Learning 265
    10.5 Case Study on Neighborhood Reconstruction 266
    10.6 Conclusion 267

    11 Transfer Learning on Graph 269
    11.1 Introduction 269
    11.2 Overview of TGL 270
    11.3 Types of TGL 273
    11.4 Key Techniques in TGL 284
    11.5 Technique Comparisons 296
    11.6 Case Studies 297
    11.7 Conclusion 299

    Part IV Emerging Trends with Case Studies 301

    12 Graph Contrastive Learning 303
    12.1 Introduction to Self-Supervised Learning 303
    12.2 Comparison with Supervised Graph Learning 311
    12.3 Fundamentals of GCL 312
    12.4 GCL Frameworks 316
    12.5 Conclusion 318

    13 Multimodal Graph Representation Learning 321
    13.1 Introduction to Multimodal GRL 321
    13.2 Types of Modalities in Graph Data 322
    13.3 Data Fusion Techniques in Multimodal GRL 325
    13.4 Applications 331
    13.5 Conclusion 334

    14 Demystifying Graph Embeddings and Industrial Applications 337
    14.1 Introduction 337
    14.2 The Importance of Graph Representation 337
    14.3 Making Sense Out of Graph-Structured Data 338
    14.4 Delineating AI Model Engineering Steps 339
    14.5 Graph Embeddings 340
    14.6 Edge Embedding Techniques 348
    14.7 Applications of Graph Embeddings 354
    14.8 Conclusion 356

    15 Knowledge Graph Foundation, Techniques, and Its Case Studies 359
    15.1 Introduction to KG 359
    15.2 Construction of a KG 365
    15.3 Techniques for Building a KG 366
    15.4 Representation and Storage 371Contents xvii
    15.5 Case Studies of KG 374
    15.6 Conclusion 383

    16 Graph Representation Learning in Wireless Communication and Tourist Movement Analysis 385
    16.1 A Graph-Theoretic Framework for Analyzing Tourist Flows from Social Media Data 385
    16.2 Knowledge-Driven Graph Learning for Next-Generation Wireless Networks 390
    16.3 Applications of GRL in Wireless Networks and Urban Tourism 395
    16.4 Other Areas of GRL Applications 397
    16.5 Conclusion 401

    References 401
    Index 403