Awesome community detection
A curated list of community detection research papers with implementations.
What is Awesome community detection?
Awesome Community Detection is a curated repository of academic research papers and their implementations related to community detection in graphs and networks. It organizes papers by methodology—such as matrix factorization, deep learning, and spectral methods—providing a structured overview of the field. The resource helps researchers and developers quickly find and apply advanced techniques for identifying cohesive groups within complex networks.
Researchers, data scientists, and machine learning engineers working on graph analysis, social network analysis, or complex systems who need a reference for state-of-the-art community detection algorithms.
It offers a uniquely organized and comprehensive collection that bridges academic research and practical implementation, saving time compared to manually searching through disparate publications. As an open-source awesome list, it benefits from community contributions to stay current.
Overview
A curated list of community detection research papers with implementations.
Use Cases
Best For
- Finding recent research papers on specific community detection methodologies
Related Projects
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