  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.
