Summary of Rdsa: a Robust Deep Graph Clustering Framework Via Dual Soft Assignment, by Yang Xiang et al.
RDSA: A Robust Deep Graph Clustering Framework via Dual Soft Assignment
by Yang Xiang, Li Fan, Tulika Saha, Xiaoying Pang, Yushan Pan, Haiyang Zhang, Chengtao Ji
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 Robust Deep Graph Clustering Framework via Dual Soft Assignment (RDSA) addresses challenges in denoising graph clustering by integrating topological features and node attributes through a node embedding module. This framework consists of three components: node embedding, structure-based soft assignment, and node-based soft assignment. RDSA demonstrates superior performance compared to existing state-of-the-art methods on various real-world datasets, exhibiting robust clustering across different graph types, including adaptability to noise, stability, and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of grouping nodes in networks is introduced, called Robust Deep Graph Clustering Framework via Dual Soft Assignment (RDSA). This method helps deal with noisy edges and improves the performance of graph clustering. It works by combining information about the network’s structure and node attributes to make better decisions when grouping nodes together. RDSA outperforms other methods on different datasets, showing its ability to handle noise and scale up for large networks. |
Keywords
» Artificial intelligence » Clustering » Embedding
