Awesome Graph Embedding
A curated collection of graph classification papers with reference implementations covering embedding, deep learning, kernels, and factorization.
What is Awesome Graph Embedding?
Awesome Graph Classification is a curated repository of research papers and reference implementations focused on graph classification methods. It covers techniques like graph embedding, deep learning, graph kernels, and matrix factorization, providing a centralized resource for anyone working with graph-structured data. The project aims to bridge the gap between theoretical research and practical application by including executable code alongside academic papers.
Researchers, data scientists, and machine learning engineers who specialize in graph-based data analysis and need access to state-of-the-art classification methods with practical implementations.
It offers a uniquely comprehensive and organized collection of graph classification resources with ready-to-use code, saving time compared to sourcing papers and implementations individually. The inclusion of benchmark datasets further enhances its utility for experimental validation and comparison.
Overview
A collection of important graph embedding, classification and representation learning papers with implementations.
Use Cases
Best For
- Researchers surveying state-of-the-art graph classification techniques
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