Summary of Hyreal: Clustering Attributed Graph Via Hyper-complex Space Representation Learning, by Junyang Chen et al.
HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learning
by Junyang Chen, Yang Lu, Mengke Li, Cuie Yang, Yiqun Zhang, Yiu-ming Cheung
First submitted to arxiv on: 22 Nov 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 paper introduces a novel approach to attributed graph clustering, addressing the Over-Smoothing effect that occurs when using Graph Convolutional Networks (GCNs) on complex data. The proposed model, called HyReaL, leverages quaternion feature transformation to enhance representation learning of attributes and bridge arbitrary-dimensional attributes to the well-developed quaternion algebra. This allows for more effective clustering and alleviates the need for stacking multiple GCN layers, which often leads to over-smoothing. Experiments demonstrate the superiority of HyReaL in downstream clustering tasks with varying numbers of clusters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with Graph Convolutional Networks (GCNs) when they’re used on complex data. The issue is called Over-Smoothing, and it means that GCNs make all the information look similar. To fix this, researchers came up with a new way to learn how to group things together based on their attributes. This approach uses special math called quaternion algebra, which helps connect different types of information. As a result, the model can do better clustering and doesn’t need as many layers, making it more powerful. |
Keywords
» Artificial intelligence » Clustering » Gcn » Representation learning
