Summary of Adversarial Training For Graph Neural Networks Via Graph Subspace Energy Optimization, by Ganlin Liu et al.
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization
by Ganlin Liu, Ziling Liang, Xiaowei Huang, Xinping Yi, Shi Jin
First submitted to arxiv on: 25 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 The recent advancement in graph neural networks (GNNs) has led to the development of novel techniques for learning over graph-structured data. However, GNNs still face challenges from adversarial topology perturbations during both training and inference phases. To address this issue, researchers have proposed various methods for improving GNN robustness, including adversarial training. This paper focuses on developing a new concept called graph subspace energy (GSE), which measures the stability of a graph’s adjacency matrix and serves as an indicator of GNN robustness against topology perturbations. The authors propose an adversarial training method that maximizes the GSE regularization term, referred to as AT-GSE, and demonstrate its effectiveness in outperforming state-of-the-art GNN adversarial training methods on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are a type of artificial intelligence that helps computers understand relationships between things. But when these networks face fake or manipulated data, they can be tricked into making wrong decisions. This paper tries to fix this problem by developing a new way to train GNNs so they’re more resistant to bad data. The idea is to measure how stable the network’s internal structure is and then use that information to make it stronger. The researchers tested their method on different types of datasets and found that it worked better than other approaches in keeping the network accurate when faced with fake data. |
Keywords
» Artificial intelligence » Gnn » Inference » Regularization
