Deep learning on graphs:
Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established meth...
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| Beteiligte Personen: | , |
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| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
[2021]
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033956204&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Zusammenfassung: | Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines |
| Umfang: | xviii, 320 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108831741 9781108924184 |
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| 520 | |a Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines | ||
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Datensatz im Suchindex
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Contents Preface Acknowledgments page xiii xvii 1 Deep Learning on Graphs: An Introduction 1.1 Introduction 1.2 Why Deep Learning on Graphs? 1.3 What Content Is Covered? 1.4 Who Should Read This Book? 1.5 Feature Learning on Graphs: A Brief History 1.5.1 Feature Selection on Graphs 1.5.2 Representation Learning on Graphs 1.6 Conclusion 1.7 Further Reading 1 1 1 3 6 8 9 10 13 13 Parti Foundations 15 2 Foundations of Graphs 2.1 Introduction 2.2 Graph Representations 2.3 Properties and Measures 2.3.1 Degree 2.3.2 Connectivity 2.3.3 Centrality 2.4 Spectral Graph Theory 2.4.1 Laplacian Matrix 2.4.2 The Eigenvalues and Eigenvectors of the Laplacian Matrix 2.5 Graph Signal Processing 17 17 18 19 19 21 23 26 26 28 29
vi Contents 2.6 2.7 2.8 2.9 3 2.5.1 Graph Fourier Transform Complex Graphs 2.6.1 Heterogeneous Graphs 2.6.2 Bipartite Graphs 2.6.3 Multidimensional Graphs 2.6.4 Signed Graphs 2.6.5 Hypergraphs 2.6.6 Dynamic Graphs Computational Tasks on Graphs 2.7.1 Node-Focused Tasks 2.7.2 Graph-Focused Tasks Conclusion Further Reading Foundations of Deep Learning 3.1 Introduction 3.2 Deep Feedforward Networks 3.2.1 The Architecture 3.2.2 Activation Functions 3.2.3 Output Layer and Loss Function 3.3 Convolutional Neural Networks 3.3.1 The Convolution Operation and Convolutional Layer 3.3.2 Convolutional Layers in Practice 3.3.3 Nonlinear Activation Layer 3.3.4 Pooling Layer 3.3.5 An Overall CNN Framework 3.4 Recurrent Neural Networks 3.4.1 The Architecture of Traditional RNNs 3.4.2 Long Short-Term Memory 3.4.3 Gated Recurrent Unit 3.5 Autoencoders 3.5.1 Undcrcomplete Autoencoders 3.5.2 Regularized Autoencoders 3.6 Training Deep Neural Networks 3.6.1 Training with Gradient Descent 3.6.2 Backpropagation 3.6.3 Preventing Overfitting 3.7 Conclusion 3.8 Further Reading 30 33 33 33 34 35 36 37 39 39 41 42 42 43 43 44 46 47 50 51 52 56 57 58 58 59 60 61 63 63 65 66 67 67 68 71 71 72
Contents Part Π Methods 4 Graph Embedding 4.1 Introduction 4.2 Graph Embedding for Simple Graphs 4.2.1 Preserving Node Co-occurrence 4.2.2 Preserving Structural Role 4.2.3 Preserving Node Status 4.2.4 Preserving Community Structure 4.3 Graph Embeddingon Complex Graphs 4.3.1 Heterogeneous Graph Embedding 4.3.2 Bipartite Graph Embedding 4.3.3 Multidimensional Graph Embedding 4.3.4 Signed Graph Embedding 4.3.5 Hypergraph Embedding 4.3.6 Dynamic Graph Embedding 4.4 Conclusion 4.5 Further Reading 5 Graph Neural Networks 5.1 Introduction 5.2 The General GNN Frameworks 5.2.1 A General Framework for Node-Focused Tasks 5.2.2 A General Framework for Graph-Focused Tasks 5.3 Graph Filters 5.3.1 Spectral-Based Graph Filters 5.3.2 Spatial-Based Graph Filters 5.4 Graph Pooling 5.4.1 Flat Graph Pooling 5.4.2 Hierarchical Graph Pooling 5.5 Parameter Learning for Graph Neural Networks 5.5.1 Parameter Learning for Node Classification 5.5.2 ParameterLearning for Graph Classification 5.6 Conclusion 5.7 Further Reading 6 Robust Graph Neural Networks 6.1 Introduction 6.2 Graph Adversarial Attacks 6.2.1 Taxonomy of Graph Adversarial Attacks 6.2.2 White-Box Attack 6.2.3 Gray-Box Attack vii 73 75 75 77 77 86 89 91 94 94 96 97 99 102 104 105 106 107 107 109 109 110 112 112 122 128 129 130 135 135 136 136 137 138 138 138 139 141 144
Contents viii 6.3 6.4 6.5 6.2.4 Black-Box Attack Graph Adversarial Defenses 6.3.1 Graph Adversarial Training 6.3.2 Graph Purification 6.3.3 Graph Attention 6.3.4 Graph Structure Learning Conclusion Further Reading 148 151 152 154 155 159 160 160 Scalable Graph Neural Networks 7.1 Introduction 7.2 Node-wise Sampling Methods 7.3 Layer-wise Sampling Methods 7.4 Subgraph-wise Sampling Methods 7.5 Conclusion 7.6 Further Reading 162 162 166 168 172 174 175 Graph Neural Networks for Complex Graphs 8.1 Introduction 8.2 Heterogeneous Graph Neural Networks 8.3 Bipartite Graph Neural Networks 8.4 Multidimensional Graph Neural Networks 8.5 Signed Graph Neural Networks 8.6 Hypergraph Neural Networks 8.7 Dynamic Graph Neural Networks 8.8 Conclusion 8.9 Further Reading 176 176 176 178 179 181 184 185 187 187 Beyond GNNs: More Deep Models on Graphs 9.1 Introduction 9.2 Autoencoders on Graphs 9.3 Recurrent Neural Networks on Graphs 9.4 Variational Autoencoders on Graphs 9.4.1 Variational Autoencoders for Node Representation Learning 9.4.2 Variational Autoencoders for Graph Generation 9.5 Generative Adversarial Networks on Graphs 9.5.1 Generative Adversarial Networks for Node Representation Learning 9.5.2 Generative Adversarial Networks for Graph Generation 188 188 189 191 193 195 196 199 200 201
Contents 9.6 9.7 Conclusion Further Reading Part Ш Applications ix 203 203 205 Graph Neural Networks in Natural Language Processing 10.1 Introduction 10.2 Semantic Role Labeling 10.3 Neural Machine Translation 10.4 Relation Extraction 10.5 Question Answering 10.5.1 The Multihop QA Task 10.5.2 Entity-GCN 10.6 Graph to Sequence Learning 10.7 Graph Neural Networks on Knowledge Graphs 10.7.1 Graph Filters for Knowledge Graphs 10.7.2 Transforming Knowledge Graphs to Simple Graphs 10.7.3 Knowledge Graph Completion 10.8 Conclusion 10.9 Further Reading 207 207 208 211 211 213 213 214 216 218 218 11 Graph Neural Networks in Computer Vision 11.1 Introduction 11.2 Visual Question Answering 11.2.1 Images as Graphs 11.2.2 Images and Questions as Graphs 11.3 Skeleton-Based Action Recognition 11.4 Image Classification 11.4.1 Zero-Shot Image Classification 11.4.2 Few-Shot Image Classification 11.4.3 Multilabel Image Classification 11.5 Point Cloud Learning 11.6 Conclusion 11.7 Further Reading 222 222 222 224 225 227 229 230 231 232 233 234 235 12 Graph Neural Networks in Data Mining 12.1 Introduction 12.2 Web Data Mining 12.2.1 Social Network Analysis 12.2.2 Recommender Systems 236 236 236 237 240 10 219 220 221 221
Contents X 12.3 12.4 12.5 12.6 13 Urban Data Mining 12.3.1 Traffic Prediction 12.3.2 Air Quality Forecasting Cybersecurity Data Mining 12.4.1 Malicious Account Detection 12.4.2 Fake News Detection Conclusion Further Reading Graph Neural Networks in Biochemistry and Health Care 13.1 Introduction 13.2 Drug Development and Discovery 13.2.1 Molecule Representation Learning 13.2.2 Protein Interface Prediction 13.2.3 Drug-Target Binding Affinity Prediction 13.3 Drug Similarity Integration 13.4 Polypharmacy Side Effect Prediction 13.5 Disease Prediction 13.6 Conclusion 13.7 Further Reading Part IV Advances 14 Advanced Topics in Graph Neural Networks 14.1 Introduction 14.2 Deeper Graph Neural Networks 14.2.1 Jumping Knowledge 14.2.2 DropEdge 14.2.3 PairNorm 14.3 Exploring Unlabeled Data via Self-Supervised Learning 14.3.1 Node-Focused Tasks 14.3.2 Graph-Focused Tasks 14.4 Expressiveness of Graph Neural Networks 14.4.1 Weisfeiler-Lehman Test 14.4.2 Expressiveness 14.5 Conclusion 14.6 Further Reading 15 Advanced Applications in Graph Neural Networks 15.1 Introduction 15.2 Combinatorial Optimization on Graphs 244 244 246 247 247 249 250 251 252 252 252 253 254 256 258 259 262 264 264 265 267 267 268 270 270 270 271 271 274 275 276 278 279 279 281 281 281
Contents 15.3 15.4 15.5 15.6 Learning Program Representations Reasoning Interacting DynamicalSystems in Physics Conclusion Further Reading Bibliography Index xi 283 285 286 286 289 315 |
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| indexdate | 2025-02-13T09:00:46Z |
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| language | English |
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| spelling | Ma, Yao ca. 20./21. Jh. Verfasser (DE-588)1244840009 aut Deep learning on graphs Yao Ma, Jiliang Tang Cambridge Cambridge University Press [2021] © 2021 xviii, 320 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines Machine learning Graph algorithms Graphentheorie (DE-588)4113782-6 gnd rswk-swf Deep Learning (DE-588)1135597375 gnd rswk-swf Graphentheorie (DE-588)4113782-6 s Deep Learning (DE-588)1135597375 s DE-604 Tang, Jiliang ca. 20./21. Jh. Verfasser (DE-588)1142538648 aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-108-83174-1 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033956204&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ma, Yao ca. 20./21. Jh Tang, Jiliang ca. 20./21. Jh Deep learning on graphs Machine learning Graph algorithms Graphentheorie (DE-588)4113782-6 gnd Deep Learning (DE-588)1135597375 gnd |
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| title | Deep learning on graphs |
| title_auth | Deep learning on graphs |
| title_exact_search | Deep learning on graphs |
| title_full | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_fullStr | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_full_unstemmed | Deep learning on graphs Yao Ma, Jiliang Tang |
| title_short | Deep learning on graphs |
| title_sort | deep learning on graphs |
| topic | Machine learning Graph algorithms Graphentheorie (DE-588)4113782-6 gnd Deep Learning (DE-588)1135597375 gnd |
| topic_facet | Machine learning Graph algorithms Graphentheorie Deep Learning |
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