$β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908285130506240 |
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| author | Aslan, Haci Ismail Wiesner, Philipp Xiong, Ping Kao, Odej |
| author_facet | Aslan, Haci Ismail Wiesner, Philipp Xiong, Ping Kao, Odej |
| contents | Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anomaly detection, and resource management. However, their vulnerability to network perturbations poses a significant challenge. We propose $β$-GNN, a model enhancing GNN robustness without sacrificing clean data performance. $β$-GNN uses a weighted ensemble, combining any GNN with a multi-layer perceptron. A learned dynamic weight, $β$, modulates the GNN's contribution. This $β$ not only weights GNN influence but also indicates data perturbation levels, enabling proactive mitigation. Experimental results on diverse datasets show $β$-GNN's superior adversarial accuracy and attack severity quantification. Crucially, $β$-GNN avoids perturbation assumptions, preserving clean data structure and performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation Aslan, Haci Ismail Wiesner, Philipp Xiong, Ping Kao, Odej Machine Learning Artificial Intelligence Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anomaly detection, and resource management. However, their vulnerability to network perturbations poses a significant challenge. We propose $β$-GNN, a model enhancing GNN robustness without sacrificing clean data performance. $β$-GNN uses a weighted ensemble, combining any GNN with a multi-layer perceptron. A learned dynamic weight, $β$, modulates the GNN's contribution. This $β$ not only weights GNN influence but also indicates data perturbation levels, enabling proactive mitigation. Experimental results on diverse datasets show $β$-GNN's superior adversarial accuracy and attack severity quantification. Crucially, $β$-GNN avoids perturbation assumptions, preserving clean data structure and performance. |
| title | $β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.20630 |