Betweenness Approximation for Edge Computing with Hypergraph Neural Networks

Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things (IoTs), given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications. Network Dismantling (ND), which is a basic problem, attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network. However, current approaches mainly focus on simple networks that model only pairwise interactions between two nodes, whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world, which can be better modeled as hypernetwork. The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork. Although some hypernetwork centrality measures (e.g., 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 a Hypergraph Neural Network (HNN). The proposed approach, called “HND”, trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way, utilizing the well-trained model to approximate the betweenness of the nodes. Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.