Graph Neural Networks

  1. Implementation and Practical Considerations
    1. Software Frameworks and Libraries
      1. PyTorch Geometric
        1. Installation and Setup
          1. Data Handling
            1. Built-in Layers
              1. Custom Layer Development
                1. Training Utilities
                2. Deep Graph Library (DGL)
                  1. Core Concepts
                    1. Message Passing API
                      1. Heterogeneous Graphs
                        1. Distributed Training
                        2. TensorFlow GNN
                          1. Graph Tensor Representation
                            1. Keras Integration
                              1. Model Building
                              2. Other Libraries
                                1. Spektral
                                  1. StellarGraph
                                    1. PyTorch Lightning
                                  2. Data Preprocessing and Management
                                    1. Graph Data Formats
                                      1. Edge Lists
                                        1. Adjacency Matrices
                                          1. Graph Markup Languages
                                            1. Standard Formats
                                            2. Data Loading and Conversion
                                              1. File I/O Operations
                                                1. Format Conversions
                                                  1. Memory Management
                                                  2. Feature Engineering
                                                    1. Node Feature Extraction
                                                      1. Edge Feature Extraction
                                                        1. Graph-level Features
                                                          1. Feature Normalization
                                                          2. Data Augmentation
                                                            1. Node Dropping
                                                              1. Edge Perturbation
                                                                1. Feature Masking
                                                                  1. Graph Mixup
                                                                2. Model Development Workflow
                                                                  1. Problem Formulation
                                                                    1. Task Definition
                                                                      1. Data Analysis
                                                                        1. Baseline Establishment
                                                                        2. Architecture Design
                                                                          1. Layer Selection
                                                                            1. Hyperparameter Choices
                                                                              1. Regularization Strategies
                                                                              2. Training Pipeline
                                                                                1. Data Loaders
                                                                                  1. Training Loops
                                                                                    1. Validation Procedures
                                                                                      1. Checkpointing
                                                                                      2. Model Evaluation
                                                                                        1. Performance Metrics
                                                                                          1. Statistical Testing
                                                                                            1. Error Analysis
                                                                                            2. Hyperparameter Optimization
                                                                                              1. Grid Search
                                                                                                1. Random Search
                                                                                                  1. Bayesian Optimization
                                                                                                    1. Population-based Training
                                                                                                  2. Deployment and Production
                                                                                                    1. Model Serving
                                                                                                      1. Batch Inference
                                                                                                        1. Real-time Inference
                                                                                                          1. API Development
                                                                                                          2. Scalability Considerations
                                                                                                            1. Hardware Requirements
                                                                                                              1. Memory Optimization
                                                                                                                1. Computational Efficiency
                                                                                                                2. Monitoring and Maintenance
                                                                                                                  1. Performance Monitoring
                                                                                                                    1. Model Drift Detection
                                                                                                                      1. Retraining Strategies
                                                                                                                      2. Integration Challenges
                                                                                                                        1. Legacy System Integration
                                                                                                                          1. Data Pipeline Integration
                                                                                                                            1. Version Control

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                                                                                                                        8. Advanced Topics and Research Frontiers

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                                                                                                                        1. Foundations for Understanding GNNs