Summary of Robgc: Towards Robust Graph Condensation, by Xinyi Gao et al.
RobGC: Towards Robust Graph Condensation
by Xinyi Gao, Hongzhi Yin, Tong Chen, Guanhua Ye, Wentao Zhang, Bin Cui
First submitted to arxiv on: 19 Jun 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 proposed Graph Condensation (GC) method, RobGC, aims to accelerate GNN training while retaining performance by generating an informative compact graph. However, existing GC methods overlook the quality of large training graphs, making the condensed graphs susceptible to noise. To address this issue, RobGC uses the condensed graph as a feedback signal to guide denoising on the original training graph, achieving mutual purification of both. This plug-and-play approach extends the robustness and applicability of condensed graphs in noisy graph structure environments. Furthermore, RobGC facilitates test-time graph denoising by calibrating the test graph’s structure using the noise-free condensed graph. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RobGC is a new way to make graph neural networks (GNNs) work better with big data. Right now, it takes too much computer power to train GNNs on large graphs. To fix this problem, researchers came up with an idea called graph condensation. This means making a smaller version of the original graph that’s easy to process and still gets good results. The trouble is, existing methods for doing this don’t account for noise in the data. Noise can make it harder for GNNs to learn what they need to know. RobGC solves this problem by using the condensed graph as a way to clean up the original data before training the GNN. This makes the GNN more robust and able to handle noisy data. |
Keywords
* Artificial intelligence * Gnn
