HyperCI: A Higher Order Collective Influence Measure for Hypernetwork Dismantling

TL;DR

HyperCI introduces a novel higher order influence measure for hypernetwork dismantling, effectively identifying critical nodes in complex systems with groupwise interactions, outperforming existing methods.

Contribution

The paper proposes HyperCI, a new higher order collective influence measure tailored for hypernetworks, addressing limitations of traditional methods on simple networks.

Findings

HyperCI outperforms baseline methods on real-world hypernetworks.

It effectively identifies critical nodes in systems with groupwise interactions.

HyperCI demonstrates superior dismantling efficiency in complex hypernetwork structures.

Abstract

The connectivity of networked systems is often dependent on a small portion of critical nodes. Network dismantling studies the strategy to identify a subset of nodes the removal of which will maximally destroy the connectivity of a network and fragment it into disconnected components. However, conventional network dismantling approaches focus on simple network which models only pairwise interaction between nodes while groupwise interactions among arbitrary number of nodes are ubiquitous in networked systems like integrated circuits. Groupwise interactions modeled by hypernetwork introduce higher order connectivity patterns, which limits the application of conventional network dismantling methods on hypernetwork. In this brief, we propose HyperCI, a higher order collective influence measure for hypernetwork dismantling. It considers the node co-occurrence characteristics and higher order influence ability both introduced by hyperedges in hypernetwork. We evaluate the effectiveness of our proposed HyperCI on six real world hypernetworks including integrated circuits and citation networks and the results indicate our proposed HyperCI outperforms baseline network dismantling methods for both simple network and hypernetwork.