This repository collects company papers and technical blogs about machine learning in production.
Awesome Community Detection
This collection links community detection research papers and their implementations.

About Awesome Community Detection
Awesome Community Detection is a curated GitHub repository that collects research papers on community detection in networks, along with their implementations. It is organized into topic sections such as matrix factorization, deep learning, spectral methods, temporal methods, block models, and hypergraphs, with a separate section for libraries.
The repository is a list of references and links, not a software tool that detects communities itself. It is open source and released under the CC0 Universal license. The homepage excerpt does not state how often the list is updated or how complete it is.
Key features
- Research papers on community detection grouped by method family
- Topic sections including deep learning, spectral methods, and block models
- Links to implementations alongside the listed research papers
- A dedicated section listing software libraries
- Tags covering graph clustering, node2vec, DeepWalk, and BigCLAM
- Content released under the CC0 Universal license
- Papers are grouped by method, which makes it easier to compare approaches
- Listed papers are paired with implementations
- Content is released under the CC0 Universal license
- It is a curated list of links and does not perform community detection itself
Awesome Community Detection at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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