Summary of Self-guided Robust Graph Structure Refinement, by Yeonjun in et al.
Self-Guided Robust Graph Structure Refinement
by Yeonjun In, Kanghoon Yoon, Kibum Kim, Kijung Shin, Chanyoung Park
First submitted to arxiv on: 19 Feb 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 This paper tackles the issue of Graph Neural Networks (GNNs) being vulnerable to adversarial attacks. Existing robust graph structure refinement (GSR) methods are limited by assumptions that don’t hold in real-world scenarios, such as clean node features and moderate structural attacks. The authors propose a self-guided GSR framework (SG-GSR) that finds a clean sub-graph within the attacked graph itself, addressing two technical challenges: loss of structural information and imbalanced node degree distribution. SG-GSR is tested under various scenarios, including non-targeted attacks, targeted attacks, feature attacks, e-commerce fraud, and noisy node labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps protect GNNs from bad data that tries to trick them. Right now, some methods can only work if the data is clean and not too messed up. The authors created a new way to make these methods better by finding a cleaner part of the bad data and training their model on that. They tested it in different situations and showed that it works well. |
