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Deep learning-based hypernetwork dismantling for effectively hindering structural recovery
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AI
DOI：10.1016/j.ipm.2025.104551.png)
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Abstract
En 中文
Network dismantling aims to disrupt network structure and function by removing the smallest set of nodes. It has been extensively adopted in many real-world applications such as preventing virus propagation and disrupting terrorist communications. Traditional approaches, however, are almost exclusively built on simple graph that capture only pairwise interactions, thereby overlooking the higher-order, group-wise dependencies that are naturally encoded by hypernetworks. In this work, we propose a hypernetwork dismantling framework based on deep learning, which can be trained purely on small synthetic hypernetworks and then applied for various real-world hypernetworks. In this framework, we also design a novel inductive hypergraph attention neural network with two-level aggregated hypergraph attention neural network layers to ensure the generalization and effectiveness of the framework. Besides, we design a novel node labeling strategy explicitly incorporating network resilience, ensuring the learned optimal hypernetwork dismantling strategy inflicts enduring structural damage, hindering recovery. Extensive experiments on two types of large synthetic and 9 real-world hypernetworks demonstrate that our framework significantly outperforms the state-of-the-art methods. Specifically, our framework achieves an overall performance improvement of 17% on synthetic hypernetworks and 20% on real-world hypernetworks. In summary, it achieves superior disruption with fewer node removals and delivers more persistent damage, fundamentally impairing system resilience.
Keywords:
Hypernetwork dismantling
Deep learning
Inductive hypergraph attention neural network
Network resilience
Journal
I
IF:
6.9
Papers:
337
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0