Summary of Graph Coarsening Via Supervised Granular-ball For Scalable Graph Neural Network Training, by Shuyin Xia et al.
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
by Shuyin Xia, Xinjun Ma, Zhiyuan Liu, Cheng Liu, Sen Zhao, Guoyin Wang
First submitted to arxiv on: 18 Dec 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 Graph Neural Networks (GNNs) have made significant progress in processing graph data, but scalability remains a major challenge. To address this, researchers have developed various graph coarsening methods. However, most existing methods are training-dependent and require a predefined coarsening rate, lacking an adaptive approach. This paper employs granular-ball computing to compress graph data effectively. It constructs a coarsened graph network by iteratively splitting the graph into granular-balls based on a purity threshold and using these granular-balls as super vertices. This process reduces the size of the original graph, enhancing training efficiency and scalability. The algorithm adapts to perform splitting without requiring a predefined coarsening rate. Experimental results show that this method achieves accuracy comparable to training on the original graph. Noise injection experiments indicate robust performance. Moreover, the approach can reduce the graph size by up to 20 times without compromising test accuracy, substantially enhancing GNN scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools for working with complex data. But they can get stuck when dealing with very large datasets. To fix this problem, scientists have developed ways to shrink big graphs into smaller ones. This paper takes a new approach by using tiny “granular-balls” to compress the graph. It’s like taking a big puzzle and breaking it down into smaller pieces that are easier to work with. The method is smart enough to adjust itself as needed, without needing someone to tell it how much to shrink the puzzle. Tests show that this new approach works just as well as working with the original big graph. It even helps GNNs learn better when there’s noise or mistakes in the data. |
Keywords
» Artificial intelligence » Gnn
