  Network dismantling aims to scratch the network into unconnected fragments 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 often disassemble the system from the perspective of
classic networks, which have only pairwise interactions, and often ignored the
more ubiquitous and nature group-wise interactions modeled by hypernetwork.
Moreover, a simple network can't describe the collective behavior of multiple
objects, it is necessary to solve related problems through hypernetwork
dismantling. In this work, we designed a higher order collective influence
measure to identify key node sets in hypernetwork. It comprehensively consider
the environment in which the target node is located and its own characteristics
to determine the importance of the node, so as to dismantle the hypernetwork by
removing these selected nodes. Finally, we used the method to carry out a
series of real-world hypernetwork dismantling tasks. Experimental results on
five real-world hypernetworks demonstrate the effectiveness of our proposed
measure.
