Summary of Hypergraph Attacks Via Injecting Homogeneous Nodes Into Elite Hyperedges, by Meixia He et al.
Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges
by Meixia He, Peican Zhu, Keke Tang, Yangming Guo
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a new framework called Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack) to address vulnerabilities in Hypergraph Neural Networks (HGNNs). Existing approaches focus on modifying hypergraphs, but overlook node spanning and group identity of hyperedges. IE-Attack addresses these limitations by identifying elite hyperedges using a node spanning approach, generating homogeneous nodes with KDE, and injecting them into the identified hyperedges. The framework improves attack performance and imperceptibility. Extensive experiments are conducted on five datasets to validate its effectiveness and superiority over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in computer science where artificial intelligence models called Hypergraph Neural Networks (HGNNs) can be tricked into making mistakes. Currently, there are limited ways to do this without being detected. The new approach, IE-Attack, makes it harder for AI models to detect and improves the way attacks are carried out. It does this by identifying important parts of the hypergraph, generating new nodes that fit in with these parts, and injecting them into the model. This is tested on five real-life datasets and shown to be more effective than existing methods. |
