This repository serves as a curated, continuously updated collection of resources on Graph Neural Networks (GNNs), with a specific focus on recommendation systems and TensorFlow implementations. It targets researchers, engineers, and practitioners interested in leveraging GNNs for complex relational data tasks.

How It Works

The repository organizes a vast array of links covering TensorFlow implementations, foundational articles, video tutorials, public datasets, recommendation algorithms, research papers, and GNN models. It emphasizes practical applications and theoretical underpinnings, providing a comprehensive overview of the GNN landscape.

Quick Start & Requirements

This repository is a curated list of links and does not require installation or execution. It serves as a knowledge base.

Highlighted Details

• Extensive coverage of GNN models and their TensorFlow implementations, including popular architectures like GraphSAGE, GAT, and RippleNet.
• A rich collection of public datasets specifically relevant to recommendation systems, spanning movies, music, books, and more.
• Links to seminal research papers and survey articles that provide theoretical foundations and state-of-the-art insights into GNNs.
• Categorization of recommendation algorithms, from traditional matrix factorization to advanced deep learning approaches.

Maintenance & Community

The repository is marked as "continually updated and refined," suggesting ongoing curation. No specific community channels or contributor information are provided in the README.

Licensing & Compatibility

The repository itself, being a collection of links, does not have a specific license. The linked resources will have their own respective licenses.

Limitations & Caveats

This is a purely informational resource; it does not provide code for GNN models or direct implementations. Users must follow the provided links to access the actual code, datasets, or research papers.