Summary of Homophily-related: Adaptive Hybrid Graph Filter For Multi-view Graph Clustering, by Zichen Wen et al.
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering
by Zichen Wen, Yawen Ling, Yazhou Ren, Tianyi Wu, Jianpeng Chen, Xiaorong Pu, Zhifeng Hao, Lifang He
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 proposes Adaptive Hybrid Graph Filter for Multi-View Graph Clustering (AHGFC), an approach that leverages both low-frequency and high-frequency signals to learn distinguishable node embeddings. Conventional graph neural networks (GNNs) are limited in their ability to perform well on heterophilous graphs, where the connected nodes do not belong to the same class. The AHGFC model uses a graph joint process and graph joint aggregation matrix to design an adaptive hybrid graph filter that is related to the homophily degree of the given graph. This allows for more effective learning of node representations. Experimental results show that AHGFC performs well on six datasets containing both homophilous and heterophilous graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are everywhere! But did you know that most graph clustering methods only work on special kinds of graphs where connected nodes belong to the same group? What about real-world graphs that don’t follow this rule? That’s what this paper is all about. The authors want to find a way to make these methods work better on more complex graphs. They propose a new approach called Adaptive Hybrid Graph Filter for Multi-View Graph Clustering (AHGFC). It’s like a special filter that helps nodes learn from each other in a way that makes sense. And the results? Pretty cool! The authors tested AHGFC on six different datasets and it worked well on all of them. |
Keywords
* Artificial intelligence * Clustering
