Graph Neural Networks: Foundations, Frontiers, and Applications
- Length: 725 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2022-02-04
- ISBN-10: 9811660530
- ISBN-13: 9789811660535
- Sales Rank: #8387139 (See Top 100 Books)
Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning.
This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs.
This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Foreword Preface Book Website and Resources To the Instructors To the Readers Acknowledgements Editor Biography List of Contributors Contents Terminologies 1 Basic concepts of Graphs 2 Machine Learning on Graphs 3 Graph Neural Networks Notations Numbers, Arrays, and Matrices Graph Basics Basic Operations Functions Probablistic Theory Part I Introduction Chapter 1 Representation Learning 1.1 Representation Learning: An Introduction 1.2 Representation Learning in Different Areas 1.2.1 Representation Learning for Image Processing 1.2.2 Representation Learning for Speech Recognition 1.2.3 Representation Learning for Natural Language Processing 1.2.4 Representation Learning for Networks 1.3 Summary Chapter 2 Graph Representation Learning 2.1 Graph Representation Learning: An Introduction 2.2 Traditional Graph Embedding 2.3 Modern Graph Embedding 2.3.1 Structure-Property Preserving Graph Representation Learning 2.3.1.1 Structure Preserving Graph Representation Learning 2.3.1.2 Property Preserving Graph Representation Learning 2.3.2 Graph Representation Learning with Side Information 2.3.3 Advanced Information Preserving Graph Representation Learning 2.4 Graph Neural Networks 2.5 Summary Chapter 3 Graph Neural Networks 3.1 Graph Neural Networks: An Introduction 3.2 Graph Neural Networks: Overview 3.2.1 Graph Neural Networks: Foundations 3.2.2 Graph Neural Networks: Frontiers 3.2.3 Graph Neural Networks: Applications 3.2.3.1 Graph Construction 3.2.3.2 Graph Representation Learning 3.2.4 Graph Neural Networks: Organization 3.3 Summary Part II Foundations of Graph Neural Networks Chapter 4 Graph Neural Networks for Node Classification 4.1 Background and Problem Definition 4.2 Supervised Graph Neural Networks 4.2.1 General Framework of Graph Neural Networks 4.2.2 Graph Convolutional Networks 4.2.3 Graph Attention Networks 4.2.4 Neural Message Passing Networks 4.2.5 Continuous Graph Neural Networks 4.3 Unsupervised Graph Neural Networks 4.3.1 Variational Graph Auto-Encoders 4.3.1.1 Problem Setup 4.3.1.2 Model 4.3.1.3 Discussion 4.3.2 Deep Graph Infomax 4.3.2.1 Problem Setup 4.3.2.2 Model 4.3.2.3 Discussion 4.4 Over-smoothing Problem 4.5 Summary Chapter 5 The Expressive Power of Graph Neural Networks 5.1 Introduction 5.2 Graph Representation Learning and Problem Formulation 5.3 The Power of Message Passing Graph Neural Networks 5.3.1 Preliminaries: Neural Networks for Sets 5.3.2 Message Passing Graph Neural Networks 5.3.3 The Expressive Power of MP-GNN 5.3.4 MP-GNN with the Power of the 1-WL Test 5.4 Graph Neural Networks Architectures that are more Powerful than 1-WL Test 5.4.1 Limitations of MP-GNN 5.4.2 Injecting Random Attributes 5.4.2.1 Relational Pooling GNN (RP-GNN) (Murphy et al, 2019a) 5.4.2.2 Random Graph Isomorphic Network (rGIN) (Sato et al, 2021) 5.4.2.3 Position-aware GNN (PGNN) (You et al, 2019) 5.4.2.4 Randomized Matrix Factorization (Srinivasan and Ribeiro, 2020a)(Dwivedi et al, 2020) 5.4.3 Injecting Deterministic Distance Attributes 5.4.3.1 Distance Encoding (Li et al, 2020e) 5.4.3.2 Identity-aware GNN (You et al, 2021) 5.4.4 Higher-order Graph Neural Networks 5.4.4.1 k-WL-induced GNNs (Morris et al, 2019) 5.4.4.2 Invariant and equivariant GNNs (Maron et al, 2018, 2019b) 5.4.4.3 FWL-induced GNNs (Maron et al, 2019a; Chen et al, 2019f) 5.5 Summary Chapter 6 Graph Neural Networks: Scalability 6.1 Introduction 6.2 Preliminary 6.3 Sampling Paradigms 6.3.1 Node-wise Sampling 6.3.1.1 GraphSAGE 6.3.1.2 VR-GCN 6.3.2 Layer-wise Sampling 6.3.2.1 FastGCN 6.3.2.2 ASGCN 6.3.3 Graph-wise Sampling 6.3.3.1 Cluster-GCN 6.3.3.2 GraphSAINT 6.3.3.3 Overall Comparison of Different Models 6.4 Applications of Large-scale Graph Neural Networks on Recommendation Systems 6.4.1 Item-item Recommendation 6.4.2 User-item Recommendation 6.5 Future Directions Chapter 7 Interpretability in Graph Neural Networks 7.1 Background: Interpretability in Deep Models 7.1.1 Definition of Interpretability and Interpretation 7.1.2 The Value of Interpretation 7.1.2.1 Model-Oriented Reasons 7.1.2.2 User-Oriented Reasons 7.1.3 Traditional Interpretation Methods 7.1.3.1 Post-Hoc Interpretation 7.1.3.2 Interpretable Modeling 7.1.4 Opportunities and Challenges 7.2 Explanation Methods for Graph Neural Networks 7.2.1 Background 7.2.2 Approximation-Based Explanation 7.2.2.1 White-Box Approximation Method 7.2.2.2 Black-Box Approximation Methods 7.2.3 Relevance-Propagation Based Explanation 7.2.4 Perturbation-Based Approaches 7.2.5 Generative Explanation 7.3 Interpretable Modeling on Graph Neural Networks 7.3.1 GNN-Based Attention Models 7.3.1.1 Attention Models for Homogeneous Graphs 7.3.1.2 Attention Models for Heterogeneous Graphs 7.3.2 Disentangled Representation Learning on Graphs 7.3.2.1 Is A Single Vector Enough? 7.3.2.2 Prototypes-Based Soft-Cluster Assignment 7.3.2.3 Dynamic Routing Based Clustering 7.4 Evaluation of Graph Neural Networks Explanations 7.4.1 Benchmark Datasets 7.4.1.1 Synthetic Datasets 7.4.1.2 Real-World Datasets 7.4.2 Evaluation Metrics 7.5 Future Directions Chapter 8 Graph Neural Networks: Adversarial Robustness 8.1 Motivation 8.2 Limitations of Graph Neural Networks: Adversarial Examples 8.2.1 Categorization of Adversarial Attacks Aspect 1: Property under Investigation (Attacker’s Goal) Aspect 2: The Perturbation Space (Attacker’s Capabilities) Aspect 3: Available Information (Attacker’s Knowledge) Aspect 4: The Algorithmic View 8.2.2 The Effect of Perturbations and Some Insights 8.2.2.1 Transferability and Patterns 8.2.3 Discussion and Future Directions 8.3 Provable Robustness: Certificates for Graph Neural Networks 8.3.1 Model-Specific Certificates Lower Bounds on the Worst-Case Margin 8.3.2 Model-Agnostic Certificates Putting Model-Agnostic Certificates into Practice 8.3.3 Advanced Certification and Discussion 8.4 Improving Robustness of Graph Neural Networks 8.4.1 Improving the Graph 8.4.2 Improving the Training Procedure 8.4.2.1 Robust Training 8.4.2.2 Further Training Principles 8.4.3 Improving the Graph Neural Networks’ Architecture 8.4.3.1 Adaptively Down-Weighting Edges 8.4.3.2 Further Approaches 8.4.4 Discussion and Future Directions 8.5 Proper Evaluation in the View of Robustness Empirical Robustness Evaluation Provable Robustness Evaluation 8.6 Summary Acknowledgements Part III Frontiers of Graph Neural Networks Chapter 9 Graph Neural Networks: Graph Classification 9.1 Introduction 9.2 Graph neural networks for graph classification: Classic works and modern architectures 9.2.1 Spatial approaches 9.2.2 Spectral approaches 9.3 Pooling layers: Learning graph-level outputs from node-level outputs 9.3.1 Attention-based pooling layers 9.3.2 Cluster-based pooling layers 9.3.3 Other pooling layers 9.4 Limitations of graph neural networks and higher-order layers for graph classification 9.4.1 Overcoming limitations 9.5 Applications of graph neural networks for graph classification 9.6 Benchmark Datasets 9.7 Summary Chapter 10 Graph Neural Networks: Link Prediction 10.1 Introduction 10.2 Traditional Link Prediction Methods 10.2.1 Heuristic Methods 10.2.1.1 Local Heuristics 10.2.1.3 Summarization 10.2.2 Latent-Feature Methods 10.2.2.1 Matrix Factorization 10.2.2.2 Network Embedding 10.2.2.3 Summarization 10.2.3 Content-Based Methods 10.3 GNN Methods for Link Prediction 10.3.1 Node-Based Methods 10.3.1.1 Graph AutoEncoder 10.3.1.2 Variational Graph AutoEncoder 10.3.1.3 Variants of GAE and VGAE 10.3.2 Subgraph-Based Methods 10.3.2.1 The SEAL Framework 10.3.2.2 Variants of SEAL 10.3.3 Comparing Node-Based Methods and Subgraph-Based Methods 10.4 Theory for Link Prediction 10.4.1 γ-Decaying Heuristic Theory 10.4.1.1 Definition of γ-Decaying Heuristic 10.4.1.2 Katz index 10.4.1.3 PageRank 10.4.1.4 SimRank 10.4.1.5 Discussion 10.4.2 Labeling Trick 10.4.2.1 Structural Representation 10.4.2.2 Labeling Trick Enables Learning Structural Representations 10.5 Future Directions 10.5.1 Accelerating Subgraph-Based Methods 10.5.2 Designing More Powerful Labeling Tricks 10.5.3 Understanding When to Use One-Hot Features Chapter 11 Graph Neural Networks: Graph Generation 11.1 Introduction 11.2 Classic Graph Generative Models 11.2.1 Erdős–Rényi Model 11.2.1.1 Model 11.2.1.2 Discussion 11.2.2 Stochastic Block Model 11.2.2.1 Model 11.2.2.2 Discussion 11.3 Deep Graph Generative Models 11.3.1 Representing Graphs 11.3.2 Variational Auto-Encoder Methods 11.3.2.1 The GraphVAE Family 11.3.2.2 Hierarchical and Constrained GraphVAEs 11.3.3 Deep Autoregressive Methods 11.3.3.1 GNN-based Autoregressive Model 11.3.3.2 Graph Recurrent Neural Networks (GraphRNN) 11.3.3.3 Graph Recurrent Attention Networks (GRAN) 11.3.4 Generative Adversarial Methods 11.3.4.1 Adjacency Matrix Based GAN 11.3.4.2 Random Walk Based GAN 11.4 Summary Chapter 12 Graph Neural Networks: Graph Transformation 12.1 Problem Formulation of Graph Transformation 12.2 Node-level Transformation 12.2.1 Definition of Node-level Transformation 12.2.2 Interaction Networks 12.2.3 Spatio-Temporal Convolution Recurrent Neural Networks 12.3 Edge-level Transformation 12.3.1 Definition of Edge-level Transformation 12.3.2 Graph Transformation Generative Adversarial Networks 12.3.3 Multi-scale Graph Transformation Networks 12.3.4 Graph Transformation Policy Networks 12.4 Node-Edge Co-Transformation 12.4.1 Definition of Node-Edge Co-Transformation 12.4.1.1 Junction-tree Variational Auto-encoder Transformer 12.4.1.2 Molecule Cycle-Consistent Adversarial Networks 12.4.1.3 Directed Acyclic Graph Transformation Networks 12.4.2 Editing-based Node-Edge Co-Transformation 12.4.2.1 Graph Convolutional Policy Networks 12.4.2.2 Molecule Deep Q-networks Transformer 12.4.2.3 Node-Edge Co-evolving Deep Graph Translator 12.5 Other Graph-based Transformations 12.5.1 Sequence-to-Graph Transformation 12.5.2 Graph-to-Sequence Transformation 12.5.3 Context-to-Graph Transformation 12.6 Summary Chapter 13 Graph Neural Networks: Graph Matching 13.1 Introduction 13.2 Graph Matching Learning 13.2.1 Problem Definition 13.2.2 Deep Learning based Models 13.2.3 Graph Neural Network based Models 13.3 Graph Similarity Learning 13.3.1 Problem Definition 13.3.2 Graph-Graph Regression Tasks 13.4 Summary Chapter 14 Graph Neural Networks: Graph Structure Learning 14.1 Introduction 14.2 Traditional Graph Structure Learning 14.2.1 Unsupervised Graph Structure Learning 14.2.1.1 Graph Structure Learning from Smooth Signals 14.2.1.2 Spectral Clustering via Graph Structure Learning 14.2.2 Supervised Graph Structure Learning 14.2.2.1 Relational Inference for Interacting Systems 14.2.2.2 Structure Learning in Bayesian Networks 14.3 Graph Structure Learning for Graph Neural Networks 14.3.1 Joint Graph Structure and Representation Learning 14.3.1.1 Problem Formulation 14.3.1.2 Learning Discrete Graph Structures 14.3.1.3 Learning Weighted Graph Structures 14.3.2 Connections to Other Problems 14.3.2.1 Graph Structure Learning as Graph Generation 14.3.2.2 Graph Structure Learning for Graph Adversarial Defenses 14.3.2.3 Understanding Transformers from a Graph Learning Perspective 14.4 Future Directions 14.4.1 Robust Graph Structure Learning 14.4.2 Scalable Graph Structure Learning 14.4.3 Graph Structure Learning for Heterogeneous Graphs 14.5 Summary Chapter 15 Dynamic Graph Neural Networks 15.1 Introduction 15.2 Background and Notation 15.2.1 Graph Neural Networks 15.2.2 Sequence Models 15.2.3 Encoder-Decoder Framework and Model Training 15.3 Categories of Dynamic Graphs 15.3.1 Discrete vs. Continues 15.3.2 Types of Evolution 15.3.3 Prediction Problems, Interpolation, and Extrapolation 15.4 Modeling Dynamic Graphs with Graph Neural Networks 15.4.1 Conversion to Static Graphs 15.4.2 Graph Neural Networks for DTDGs 15.4.3 Graph Neural Networks for CTDGs 15.5 Applications 15.5.1 Skeleton-based Human Activity Recognition 15.5.2 Traffic Forecasting 15.5.3 Temporal Knowledge Graph Completion 15.6 Summary Chapter 16 Heterogeneous Graph Neural Networks 16.1 Introduction to HGNNs 16.1.1 Basic Concepts of Heterogeneous Graphs 16.1.2 Challenges of HG Embedding 16.1.3 Brief Overview of Current Development 16.2 Shallow Models 16.2.1 Decomposition-based Methods 16.2.2 Random Walk-based Methods 16.3 Deep Models 16.3.1 Message Passing-based Methods (HGNNs) 16.3.2 Encoder-decoder-based Methods 16.3.3 Adversarial-based Methods 16.4 Review 16.5 Future Directions 16.5.1 Structures and Properties Preservation 16.5.2 Deeper Exploration 16.5.3 Reliability 16.5.4 Applications Chapter 17 Graph Neural Networks: AutoML 17.1 Background 17.1.1 Notations of AutoGNN 17.1.2 Problem Definition of AutoGNN 17.1.3 Challenges in AutoGNN 17.2 Search Space 17.2.1 Architecture Search Space 17.2.1.1 Micro-architecture Search Space 17.2.1.2 Macro-architecture Search Space 17.2.2 Training Hyperparameter Search Space 17.2.3 Efficient Search Space 17.3 Search Algorithms 17.3.1 Random Search 17.3.2 Evolutionary Search 17.3.3 Reinforcement Learning Based Search 17.3.4 Differentiable Search 17.3.5 Efficient Performance Estimation 17.4 Future Directions Acknowledgements Chapter 18 Graph Neural Networks: Self-supervised Learning 18.1 Introduction 18.2 Self-supervised Learning 18.3 Applying SSL to Graph Neural Networks: Categorizing Training Strategies, Loss Functions and Pretext Tasks 18.3.1 Training Strategies 18.3.1.1 Self-training 18.3.1.2 Pre-training and Fine-tuning 18.3.1.3 Joint Training 18.3.2 Loss Functions 18.3.2.1 Classification and Regression Loss 18.3.2.2 Contrastive Learning Loss 18.3.3 Pretext Tasks 18.4 Node-level SSL Pretext Tasks 18.4.1 Structure-based Pretext Tasks 18.4.2 Feature-based Pretext Tasks 18.4.3 Hybrid Pretext Tasks 18.5 Graph-level SSL Pretext Tasks 18.5.1 Structure-based Pretext Tasks 18.5.2 Feature-based Pretext Tasks 18.5.3 Hybrid Pretext Tasks 18.6 Node-graph-level SSL Pretext Tasks 18.7 Discussion 18.8 Summary Part IV Broad and Emerging Applications with Graph Neural Networks Chapter 19 Graph Neural Networks in Modern Recommender Systems 19.1 Graph Neural Networks for Recommender System in Practice 19.1.1 Introduction 19.1.2 Classic Approaches to Predict User-Item Preference 19.1.3 Item Recommendation in user-item Recommender Systems: a Bipartite Graph Perspective 19.2 Case Study 1: Dynamic Graph Neural Networks Learning 19.2.1 Dynamic Sequential Graph 19.2.2 DSGL: Dynamic Sequential Graph Learning 19.2.2.1 Overview 19.2.2.2 Embedding Layer 19.2.2.3 Time-Aware Sequence Encoding 19.2.2.4 Second-Order Graph Attention 19.2.2.5 Aggregation and Layer Combination 19.2.3 Model Prediction 19.2.4 Experiments and Discussions 19.2.4.1 Performance Comparison 19.2.4.2 Effectiveness of Graph Structure and Layer Combination 19.2.4.3 Effectiveness of Time-Aware Sequence Encoding 19.2.4.4 Effectiveness of Second-Order Graph Attention 19.3 Case Study 2: Device-Cloud Collaborative Learning for Graph Neural Networks 19.3.1 The proposed framework 19.3.1.1 MetaPatch for On-device Personalization 19.3.1.2 MoMoDistill to Enhance the Cloud Modeling 19.3.2 Experiments and Discussions 19.3.2.1 How is the performance of DCCL compared with the SOTAs? 19.3.2.2 Whether on-device personalization benefits to the cloud model? 19.3.2.3 The iterative characteristics of the multi-round DCCL. 19.3.2.4 Ablation Study of DCCL 19.4 Future Directions Chapter 20 Graph Neural Networks in Computer Vision 20.1 Introduction 20.2 Representing Vision as Graphs 20.2.1 Visual Node representation 20.2.2 Visual Edge representation 20.2.2.1 Spatial Edges 20.2.2.2 Temporal Edges 20.3 Case Study 1: Image 20.3.1 Object Detection 20.3.2 Image Classification 20.4 Case Study 2: Video 20.4.1 Video Action Recognition 20.4.2 Temporal Action Localization 20.5 Other Related Work: Cross-media 20.5.1 Visual Caption 20.5.2 Visual Question Answering 20.5.3 Cross-Media Retrieval 20.6 Frontiers for Graph Neural Networks on Computer Vision 20.6.1 Advanced Graph Neural Networks for Computer Vision 20.6.2 Broader Area of Graph Neural Networks on Computer Vision 20.7 Summary Chapter 21 Graph Neural Networks in Natural Language Processing 21.1 Introduction 21.2 Modeling Text as Graphs 21.2.1 Graph Representations in Natural Language Processing 21.2.2 Tackling Natural Language Processing Tasks from a Graph Perspective 21.3 Case Study 1: Graph-based Text Clustering and Matching 21.3.1 Graph-based Clustering for Hot Events Discovery and Organization 21.3.2 Long Document Matching with Graph Decomposition and Convolution 21.4 Case Study 2: Graph-based Multi-Hop Reading Comprehension 21.5 Future Directions 21.6 Conclusions Chapter 22 Graph Neural Networks in Program Analysis 22.1 Introduction 22.2 Machine Learning in Program Analysis 22.3 A Graph Represention of Programs 22.4 Graph Neural Networks for Program Graphs 22.5 Case Study 1: Detecting Variable Misuse Bugs 22.6 Case Study 2: Predicting Types in Dynamically Typed Languages 22.7 Future Directions Chapter 23 Graph Neural Networks in Software Mining 23.1 Introduction 23.2 Modeling Software as a Graph 23.2.1 Macro versus Micro Representations 23.2.1.1 Macro-level Representations 23.2.1.2 Micro-level Representations 23.2.2 Combining the Macro- and Micro-level 23.3 Relevant Software Mining Tasks 23.4 Example Software Mining Task: Source Code Summarization 23.4.1 Primer GNN-based Code Summarization 23.4.1.1 Model Input / Output 23.4.1.2 Model Architecture 23.4.1.3 Experiment 23.4.1.4 What benefit did the GNN bring? 23.4.2 Directions for Improvement 23.4.2.1 Example Micro-level Improvement 23.4.2.2 Example Macro-level Improvement 23.5 Summary Chapter 24 GNN-based Biomedical Knowledge Graph Mining in Drug Development 24.1 Introduction 24.2 Existing Biomedical Knowledge Graphs 24.3 Inference on Knowledge Graphs 24.3.1 Conventional KG inference techniques 24.3.2 GNN-based KG inference techniques 24.4 KG-based hypothesis generation in computational drug development 24.4.1 A machine learning framework for KG-based drug repurposing 24.4.2 Application of KG-based drug repurposing in COVID-19 24.5 Future directions 24.5.1 KG quality control 24.5.2 Scalable inference 24.5.3 Coupling KGs with other biomedical data Chapter 25 Graph Neural Networks in Predicting Protein Function and Interactions 25.1 From Protein Interactions to Function: An Introduction 25.1.1 Enter Stage Left: Protein-Protein Interaction Networks 25.1.2 Problem Formulation(s), Assumptions, and Noise: A Historical Perspective 25.1.3 Shallow Machine Learning Models over the Years 25.1.4 Enter Stage Right: Graph Neural Networks 25.1.4.1 Preliminaries 25.1.4.2 GNNs for Representation Learning 25.1.4.3 GNNs for the Link Prediction Problem 25.1.4.4 GNNs for Automated Function Prediction as a Multi-label Classification Problem 25.2 Highlighted Case Studies 25.2.1 Case Study 1: Prediction of Protein-Protein and Protein-Drug Interactions: The Link Prediction Problem 25.2.2 Case Study 2: Prediction of Protein Function and Functionally-important Residues 25.2.3 Case Study 3: From Representation Learning to Multirelational Link Prediction in Biological Networks with Graph Autoencod 25.3 Future Directions Chapter 26 Graph Neural Networks in Anomaly Detection 26.1 Introduction 26.2 Issues 26.2.1 Data-specific issues 26.2.2 Task-specific Issues 26.2.3 Model-specific Issues 26.3 Pipeline 26.3.1 Graph Construction and Transformation 26.3.2 Graph Representation Learning 26.3.3 Prediction 26.4 Taxonomy 26.5 Case Studies 26.5.1 Case Study 1: Graph Embeddings for Malicious Accounts Detection 26.5.2 Case Study 2: Hierarchical Attention Mechanism based Cash-out User Detection 26.5.3 Case Study 3: Attentional Heterogeneous Graph Neural Networks for Malicious Program Detection 26.5.4 Case Study 4: Graph Matching Framework to Learn the Program Representation and Similarity Metric via Graph Neural Network 26.5.5 Case Study 5: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN 26.5.6 Case Study 6: GCN-based Anti-Spam for Spam Review Detection 26.6 Future Directions Chapter 27 Graph Neural Networks in Urban Intelligence 27.1 Graph Neural Networks for Urban Intelligence 27.1.1 Introduction 27.1.2 Application scenarios in urban intelligence 27.1.3 Representing urban systems as graphs 27.1.4 Case Study 1: Graph Neural Networksin urban configuration and transportation 27.1.5 Case Study 2: Graph Neural Networks in urban anomaly and event detection 27.1.6 Case Study 3: Graph Neural Networks in urban human behavior inference 27.1.7 Future Directions References
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