Summary of Graph Clustering with Cross-view Feature Propagation, by Zhixuan Duan et al.
Graph Clustering with Cross-View Feature Propagation
by Zhixuan Duan, Zuo Wang, Fanghui Bi
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 method, Graph Clustering With Cross-View Feature Propagation (GCCFP), leverages multi-view feature propagation to enhance cluster identification in graph data. Unlike previous approaches, this novel method employs a unified objective function that utilizes graph topology and multi-view vertex features to determine vertex cluster membership. The authors derive an iterative algorithm to optimize this function, prove model convergence within a finite number of iterations, and analyze its computational complexity. Experimental results on various real-world graphs demonstrate the superior clustering performance of GCCFP compared to well-established methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph clustering is a way to group similar things in a network. Instead of just looking at how connected they are, this new method looks at multiple features to figure out which groups belong together. It’s like using different lenses to see the same picture and getting a better understanding of what’s going on. The researchers created a special algorithm that uses these different views to group things correctly. They tested it on real-world networks and found that it works much better than other methods. |
Keywords
» Artificial intelligence » Clustering » Objective function
