Summary of Similarity-enhanced Homophily For Multi-view Heterophilous Graph Clustering, by Jianpeng Chen et al.
SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering
by Jianpeng Chen, Yawen Ling, Yazhou Ren, Zichen Wen, Tianyi Wu, Shufei Zhang, Lifang He
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents a novel approach called SiMilarity-enhanced Homophily for Multi-view Heterophilous Graph Clustering (SMHGC) that addresses the limitations of existing methods in handling heterophilous graphs. The proposed method analyzes the relationship between similarity and graph homophily, introducing three similarity terms to enhance homophily in a label-free manner. A consensus-based inter- and intra-view fusion paradigm is then used to fuse improved homophilous graphs from different views for clustering. Experimental results on both multi-view heterophilous and homophilous datasets demonstrate the strong capacity of SMHGC for unsupervised multi-view heterophilous graph learning, with consistent performance across semi-synthetic datasets with varying levels of homophily. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to group things that are connected in different ways. Right now, we can only do this when the connections are mostly between similar things. But what if the connections are also between very different things? The authors came up with a solution called SMHGC, which looks at how similar things are and uses that information to group them better. They tested it on some datasets and found that it works really well, even when the connections aren’t just between similar things. |
Keywords
» Artificial intelligence » Clustering » Unsupervised
