Summary of Modularity Aided Consistent Attributed Graph Clustering Via Coarsening, by Samarth Bhatia (1) et al.
Modularity aided consistent attributed graph clustering via coarsening
by Samarth Bhatia, Yukti Makhija, Manoj Kumar, Sandeep Kumar
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the limitations of current graph clustering methods by proposing a novel approach that integrates coarsening and modularity maximization to detect communities with high accuracy. The method uses a loss function combining log-determinant, smoothness, and modularity components, which is optimized using a block majorization-minimization technique. This yields superior clustering outcomes compared to existing state-of-the-art methods for both attributed and non-attributed graphs. The proposed algorithm is theoretically consistent under the Degree-Corrected Stochastic Block Model (DC-SBM), ensuring asymptotic error-free performance and complete label recovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to group similar nodes in graphs based on their connections and features. Currently, it’s hard to find communities that are meaningful and accurate. The researchers combined two techniques – coarsening and modularity maximization – to make the process more efficient and effective. They also created a special formula to measure how well the groups fit together. This new approach can be used with other machine learning tools like graph neural networks or variational graph autoencoders. It works really well on many different types of graphs and is better than what we have now. |
Keywords
» Artificial intelligence » Clustering » Loss function » Machine learning
