Hypernetwork Dismantling via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2104.14332v1
• Date: Thu, 29 Apr 2021 13:35:29 GMT
• Title: Hypernetwork Dismantling via Deep Reinforcement Learning
• Authors: Dengcheng Yan, Wenxin Xie, Yiwen Zhang
• Abstract summary: We formulate the hypernetwork dismantling problem as a node sequence decision problem. We propose a deep reinforcement learning-based hypernetwork dismantling framework. Experimental results on five real-world hypernetworks demonstrate the effectiveness of our proposed framework.
• Score: 1.4877837830677472
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Network dismantling aims to degrade the connectivity of a network by removing an optimal set of nodes and 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. Generally, our framework builds an agent. It 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 the 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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