  Edge computing is highly demanded to achieve their full potentials Internet
of Things (IoT), since various IoT systems have been generating big data
facilitating modern latency-sensitive applications. As a basic problem, network
dismantling tries to find an optimal set of nodes of which will maximize the
connectivity degradation in a network. However, current approaches mainly focus
on simple networks modeling only pairwise interactions between two nodes, while
higher order groupwise interactions among arbitrary number of nodes are
ubiquitous in real world which can be better modeled as hypernetwork. The
structural difference between simple network and hypernetwork restricts the
direct application of simple network dismantling methods to hypernetwork. Even
though some hypernetwork centrality measures such as betweenness can be used
for hypernetwork dismantling, they face the problem of balancing effectiveness
and efficiency. Therefore, we propose a betweenness approximation-based
hypernetwork dismantling method with hypergraph neural network, namely HND. HND
trains a transferable hypergraph neural network-based regression model on
plenty of generated small-scale synthetic hypernetwork in a supervised way, and
utilizes the well-trained model to approximate nodes' betweenness. Extensive
experiments on five real hypernetworks demonstrate the effectiveness and
efficiency of HND comparing with various baselines.
