Awesome graph classification
A curated collection of graph classification papers with implementations covering embeddings, deep learning, kernels, and factorization.
What is Awesome graph classification?
Awesome Graph Classification is a curated collection of research papers and reference implementations focused on graph classification methods. It covers approaches including graph embeddings, deep learning models, graph kernels, and matrix factorization techniques for analyzing graph-structured data. The repository serves as a comprehensive resource for understanding and applying state-of-the-art graph classification algorithms.
Machine learning researchers, data scientists, and practitioners working with graph-structured data who need to implement or benchmark graph classification methods. It's particularly valuable for those entering the field of graph machine learning or looking for reproducible implementations of academic papers.
This collection provides carefully curated papers with working implementations, saving researchers time searching for reliable code. Unlike generic paper lists, it focuses specifically on graph classification with practical implementations, making it easier to reproduce results and build upon existing research.
Overview
A collection of important graph embedding, classification and representation learning papers with implementations.
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
