MONA: An Efficient and Scalable Strategy for Targeted k-Nodes Collapse

TL;DR

This paper introduces MONA, a scalable strategy for targeted removal of edges to cause specific nodes in a network's k-core to collapse, addressing a new vulnerability in network robustness.

Contribution

The paper proposes a novel algorithm MOD for candidate reduction and an efficient strategy MONA for targeted k-node collapse, advancing network vulnerability analysis.

Findings

MONA outperforms several baselines in effectiveness

MONA demonstrates high scalability on large networks

Experimental results validate the approach's efficiency

Abstract

The concept of k-core plays an important role in measuring the cohesiveness and engagement of a network. And recent studies have shown the vulnerability of k-core under adversarial attacks. However, there are few researchers concentrating on the vulnerability of individual nodes within k-core. Therefore, in this paper, we attempt to study Targeted k-Nodes Collapse Problem (TNsCP), which focuses on removing a minimal size set of edges to make multiple target k-nodes collapse. For this purpose, we first propose a novel algorithm named MOD for candidate reduction. Then we introduce an efficient strategy named MONA, based on MOD, to address TNsCP. Extensive experiments validate the effectiveness and scalability of MONA compared to several baselines. An open-source implementation is available at https://github.com/Yocenly/MONA.