This repository serves as a curated collection of research and code related to the application of Graph Neural Networks (GNNs) in communication networks. It targets researchers and engineers in the telecommunications and networking fields, providing a comprehensive overview of GNNs' utility for tasks such as routing optimization, resource allocation, anomaly detection, and traffic prediction.

How It Works

The project focuses on leveraging the inherent graph structure of communication networks to apply GNNs. By representing network components (nodes) and their connections (edges) as graphs, GNNs can effectively learn complex relationships and patterns. This approach allows for more sophisticated modeling and optimization compared to traditional methods, enabling better performance in various network management and control tasks.

Quick Start & Requirements

This repository is a collection of literature and code pointers, not a runnable software package. Specific code examples would require individual setup based on their respective READMEs and dependencies (e.g., Python, PyTorch/TensorFlow, specific communication libraries).

Highlighted Details

• Extensive compilation of academic papers and code repositories applying GNNs to diverse communication network problems.
• Covers a wide range of applications including routing, resource allocation, security (intrusion detection), traffic prediction, and more.
• Includes links to surveys, tools (like IGNNITION), and competition resources for deeper engagement.
• Regularly updated list of literature, reflecting the latest advancements in the field.

Maintenance & Community

The repository is maintained by jwwthu and appears to be a personal academic collection. It cites several research papers and includes links to WeChat and Zhihu accounts for community engagement related to network and communication topics.

Licensing & Compatibility

The repository itself does not specify a license. Individual code repositories linked within would have their own licenses.

Limitations & Caveats

This repository is a curated list of resources and does not provide a unified, executable framework. Users must refer to individual linked projects for specific functionalities, dependencies, and usage instructions.