Summary of Can Large Language Models Improve the Adversarial Robustness Of Graph Neural Networks?, by Zhongjian Zhang et al.
Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
by Zhongjian Zhang, Xiao Wang, Huichi Zhou, Yue Yu, Mengmei Zhang, Cheng Yang, Chuan Shi
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study explores the potential of large language models (LLMs) to improve the robustness of graph neural networks (GNNs) against topology perturbations. The researchers find that while LLMs can enhance the performance of GNNs, they still remain vulnerable to attacks, with an average decrease in accuracy of 23.1%. To address this issue, the study proposes an LLM-based framework, called LLM4RGNN, which distills the inference capabilities of GPT-4 into a local LLM for identifying malicious edges and an LM-based edge predictor for finding missing important edges. The framework is designed to recover a robust graph structure, improving the accuracy of GNNs even in scenarios with high perturbation ratios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools that can analyze complex relationships between data points, but they’re vulnerable to attacks that manipulate these connections. Recent breakthroughs in large language models have raised hopes that these models could help make GNNs more robust. But how effective would this approach be? A new study finds that while LLMs can improve GNN performance, there’s still a significant decrease in accuracy when faced with malicious attacks. The researchers propose a solution by developing an LLM-based framework to recover the original graph structure and improve GNN reliability. |
Keywords
* Artificial intelligence * Gnn * Gpt * Inference
