Summary of Talos: a More Effective and Efficient Adversarial Defense For Gnn Models Based on the Global Homophily Of Graphs, by Duanyu Li et al.
Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs
by Duanyu Li, Huijun Wu, Min Xie, Xugang Wu, Zhenwei Wu, Wenzhe Zhang
First submitted to arxiv on: 6 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 In this paper, researchers tackle the challenge of defending Graph Neural Network (GNN) models against adversarial attacks on real-world scale graph data. GNNs are susceptible to minor perturbations in graph data inducing substantial alterations in model predictions. The authors propose a new defense method, Talos, which enhances global homophily of graphs as a defense. Compared to state-of-the-art approaches, Talos outperforms while imposing little computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to protect GNN models from fake data that tries to trick them. Right now, there are no good ways to do this on really big graph datasets. The problem is that most methods either focus too much on what’s happening in small parts of the graph or take too long to work. The authors of this paper came up with a new way called Talos that makes the whole graph more similar and works well without taking too long. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
