6lyc/CDNMF
[ICASSP 2024] Official implementation of our paper "Contrastive Deep Nonnegative Matrix Factorization for Community Detection"
This project helps researchers and data scientists analyze complex network data, such as citation networks or social graphs, to identify underlying communities or groups. You input a text file representing a network's structure and node attributes, and it outputs a clearer understanding of how nodes group together. It's designed for anyone needing to uncover hidden structures in large, interconnected datasets.
No commits in the last 6 months.
Use this if you need to accurately identify distinct communities within a network where nodes also have descriptive attributes, beyond just their connections.
Not ideal if your network data is very small, lacks node attributes, or if you primarily need to visualize networks without deeply analyzing community structures.
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42
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Language
Python
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Last pushed
May 22, 2025
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