Graph Neural Networks

9.

Implementation and Practical Considerations

9.1.

Software Frameworks and Libraries

9.1.1.

PyTorch Geometric

9.1.1.1.

Installation and Setup

9.1.1.2.

9.1.1.3.

Built-in Layers

9.1.1.4.

Custom Layer Development

9.1.1.5.

Training Utilities

9.1.2.

Deep Graph Library (DGL)

9.1.2.1.

9.1.2.2.

Message Passing API

9.1.2.3.

Heterogeneous Graphs

9.1.2.4.

Distributed Training

9.1.3.

9.1.3.1.

Graph Tensor Representation

9.1.3.2.

Keras Integration

9.1.3.3.

9.1.4.

Other Libraries

9.1.4.1.

9.1.4.2.

9.1.4.3.

PyTorch Lightning

9.2.

Data Preprocessing and Management

9.2.1.

Graph Data Formats

9.2.1.1.

9.2.1.2.

Adjacency Matrices

9.2.1.3.

Graph Markup Languages

9.2.1.4.

Standard Formats

9.2.2.

Data Loading and Conversion

9.2.2.1.

File I/O Operations

9.2.2.2.

Format Conversions

9.2.2.3.

Memory Management

9.2.3.

Feature Engineering

9.2.3.1.

Node Feature Extraction

9.2.3.2.

Edge Feature Extraction

9.2.3.3.

Graph-level Features

9.2.3.4.

Feature Normalization

9.2.4.

Data Augmentation

9.2.4.1.

9.2.4.2.

Edge Perturbation

9.2.4.3.

Feature Masking

9.2.4.4.

9.3.

Model Development Workflow

9.3.1.

Problem Formulation

9.3.1.1.

Task Definition

9.3.1.2.

9.3.1.3.

Baseline Establishment

9.3.2.

Architecture Design

9.3.2.1.

Layer Selection

9.3.2.2.

Hyperparameter Choices

9.3.2.3.

Regularization Strategies

9.3.3.

Training Pipeline

9.3.3.1.

9.3.3.2.

9.3.3.3.

Validation Procedures

9.3.3.4.

9.3.4.

Model Evaluation

9.3.4.1.

Performance Metrics

9.3.4.2.

Statistical Testing

9.3.4.3.

9.3.5.

Hyperparameter Optimization

9.3.5.1.

9.3.5.2.

9.3.5.3.

Bayesian Optimization

9.3.5.4.

Population-based Training

9.4.

Deployment and Production

9.4.1.

9.4.1.1.

Batch Inference

9.4.1.2.

Real-time Inference

9.4.1.3.

API Development

9.4.2.

Scalability Considerations

9.4.2.1.

Hardware Requirements

9.4.2.2.

Memory Optimization

9.4.2.3.

Computational Efficiency

9.4.3.

Monitoring and Maintenance

9.4.3.1.

Performance Monitoring

9.4.3.2.

Model Drift Detection

9.4.3.3.

Retraining Strategies

9.4.4.

Integration Challenges

9.4.4.1.

Legacy System Integration

9.4.4.2.

Data Pipeline Integration

9.4.4.3.

Version Control

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8. Advanced Topics and Research Frontiers

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1. Foundations for Understanding GNNs