  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.
