Summary of Sshpool: the Separated Subgraph-based Hierarchical Pooling, by Zhuo Xu et al.
SSHPool: The Separated Subgraph-based Hierarchical Pooling
by Zhuo Xu, Lixin Cui, Ming Li, Yue Wang, Ziyu Lyu, Hangyuan Du, Lu Bai, Philip S. Yu, Edwin R. Hancock
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 local graph pooling method called Separated Subgraph-based Hierarchical Pooling (SSHPool) for graph classification tasks. The approach starts by clustering nodes in a sample graph, creating separate subgraphs that can be processed independently using local graph convolution units. This design choice helps avoid the over-smoothing problem common in Graph Neural Networks (GNNs). By hierarchically applying this process to coarsened graphs, SSHPool extracts hierarchical global features and captures intrinsic structural characteristics. The authors develop an end-to-end GNN framework that incorporates SSHPool for graph classification tasks. Experimental results demonstrate the superior performance of the proposed model on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to analyze graph data by breaking it down into smaller parts and processing each part separately. It helps prevent mistakes that can happen when analyzing big graphs, making it more accurate. The authors also test their method on different types of data and show it works better than other methods. |
Keywords
* Artificial intelligence * Classification * Clustering * Gnn
