Summary of Pasco (parallel Structured Coarsening): An Overlay to Speed Up Graph Clustering Algorithms, by Etienne Lasalle (ockham) et al.
PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms
by Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat, Rémi Gribonval, Paulo Gonçalves, Màrton Karsai
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper introduces PASCO, a novel framework for accelerating clustering algorithms on large graphs with complex community structures. By first coarsening the input graph into smaller subgraphs, then applying clustering algorithms in parallel, and finally aligning and combining the results using an optimal transport method, PASCO achieves significant improvements in computational efficiency, structural preservation, and output partition quality compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PASCO is a new way to make computers work faster when finding groups in big networks. It works by breaking down the network into smaller pieces, doing some clustering on each piece, and then putting all the answers together to get a good final answer. This makes it much faster and better than other methods. |
Keywords
» Artificial intelligence » Clustering
