Graph Neural Networks

  1. Advanced Topics and Research Frontiers
    1. Theoretical Foundations
      1. Expressive Power of GNNs
        1. Weisfeiler-Leman Hierarchy
          1. Universal Approximation Properties
            1. Limitations and Impossibility Results
            2. Generalization Theory
              1. PAC-Bayes Bounds
                1. Rademacher Complexity
                  1. Stability Analysis
                  2. Optimization Theory
                    1. Convergence Analysis
                      1. Landscape Analysis
                        1. Non-convex Optimization
                      2. Robustness and Adversarial Attacks
                        1. Adversarial Attacks on GNNs
                          1. Node Feature Attacks
                            1. Graph Structure Attacks
                              1. Evasion vs Poisoning Attacks
                              2. Defense Mechanisms
                                1. Adversarial Training
                                  1. Graph Purification
                                    1. Certified Defenses
                                    2. Robustness Evaluation
                                      1. Attack Success Metrics
                                        1. Robustness Certificates
                                      2. Explainability and Interpretability
                                        1. Instance-level Explanations
                                          1. Node Importance
                                            1. Edge Importance
                                              1. Subgraph Explanations
                                              2. Model-level Explanations
                                                1. Feature Importance
                                                  1. Layer-wise Analysis
                                                    1. Attention Visualization
                                                    2. Explanation Methods
                                                      1. GNNExplainer
                                                        1. GraphLIME
                                                          1. PGExplainer
                                                          2. Evaluation of Explanations
                                                            1. Fidelity Metrics
                                                              1. Human Studies
                                                                1. Ground Truth Comparisons
                                                              2. Fairness in GNNs
                                                                1. Bias Sources in Graph Data
                                                                  1. Historical Bias
                                                                    1. Representation Bias
                                                                      1. Measurement Bias
                                                                      2. Fairness Metrics
                                                                        1. Individual Fairness
                                                                          1. Group Fairness
                                                                            1. Counterfactual Fairness
                                                                            2. Fair GNN Methods
                                                                              1. Pre-processing Approaches
                                                                                1. In-processing Approaches
                                                                                  1. Post-processing Approaches
                                                                                2. Privacy-Preserving GNNs
                                                                                  1. Privacy Threats
                                                                                    1. Node Re-identification
                                                                                      1. Attribute Inference
                                                                                      2. Privacy-Preserving Techniques
                                                                                        1. Differential Privacy
                                                                                          1. Federated Learning
                                                                                            1. Secure Multi-party Computation
                                                                                            2. Privacy-Utility Trade-offs
                                                                                            3. Dynamic and Temporal GNNs
                                                                                              1. Temporal Graph Representation
                                                                                                1. Discrete Time Models
                                                                                                  1. Continuous Time Models
                                                                                                    1. Event-based Models
                                                                                                    2. Dynamic GNN Architectures
                                                                                                      1. Recurrent GNNs
                                                                                                        1. Temporal Attention
                                                                                                          1. Memory-augmented GNNs
                                                                                                          2. Streaming Graph Processing
                                                                                                            1. Online Learning
                                                                                                              1. Incremental Updates
                                                                                                                1. Concept Drift
                                                                                                              2. Generative Models for Graphs
                                                                                                                1. Autoregressive Models
                                                                                                                  1. Sequential Generation
                                                                                                                    1. Conditional Generation
                                                                                                                    2. Variational Autoencoders
                                                                                                                      1. Graph VAE
                                                                                                                        1. Conditional VAE
                                                                                                                        2. Generative Adversarial Networks
                                                                                                                          1. GraphGAN
                                                                                                                            1. MolGAN
                                                                                                                            2. Flow-based Models
                                                                                                                              1. Normalizing Flows
                                                                                                                                1. Graph Normalizing Flows
                                                                                                                                2. Diffusion Models
                                                                                                                                  1. Graph Diffusion Models
                                                                                                                                    1. Conditional Generation

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                                                                                                                                7. Evaluation and Benchmarking

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                                                                                                                                9. Implementation and Practical Considerations