Hypernetwork Dismantling via Deep Reinforcement Learning

计算机科学 强化学习 嵌入 成对比较 人工智能 理论计算机科学
作者
Dengcheng Yan,Wenxin Xie,Yiwen Zhang
出处
期刊:IEEE Transactions on Network Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:9 (5): 3302-3315
标识
DOI:10.1109/tnse.2022.3174163
摘要

Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes. It has been widely adopted in many real-world applications such as epidemic control and rumor containment. However, conventional methods usually focus on simple network modeling with only pairwise interactions, while group-wise interactions modeled by hypernetwork are ubiquitous and critical. In this work, we formulate the hypernetwork dismantling problem as a node sequence decision problem and propose a deep reinforcement learning (DRL)-based hypernetwork dismantling framework. Besides, we design a novel inductive hypernetwork embedding method to ensure the transferability to various real-world hypernetworks. Our framework first generates small-scale synthetic hypernetworks and embeds the nodes and hypernetworks into a low dimensional vector space to represent the action and state space in DRL, respectively. Then trial-and-error dismantling tasks are conducted by an agent on these synthetic hypernetworks, and the dismantling strategy is continuously optimized. Finally, the well-optimized strategy is applied to real-world hypernetwork dismantling tasks. Experimental results on five real-world hypernetworks demonstrate the effectiveness of our proposed framework.
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