Targeted k-node Collapse Problem: Towards Understanding the Robustness of Local k-core Structure

TL;DR

This paper introduces the Targeted k-node Collapse Problem (TNCP), analyzing how many edges need removal to cause a specific node's collapse in a k-core, and proposes heuristic algorithms to address this NP-hard problem.

Contribution

It formally defines TNCP, proves its NP-hardness, and proposes heuristic algorithms TNC and ATNC for large-scale network analysis.

Findings

Algorithms outperform baseline methods in experiments

TNCP effectively measures k-core resilience

Proves NP-hardness of TNCP

Abstract

The concept of k-core, which indicates the largest induced subgraph where each node has k or more neighbors, plays a significant role in measuring the cohesiveness and the engagement of a network, and it is exploited in diverse applications, e.g., network analysis, anomaly detection, community detection, etc. Recent works have demonstrated the vulnerability of k-core under malicious perturbations which focuses on removing the minimal number of edges to make a whole k-core structure collapse. However, to the best of our knowledge, there is no existing research concentrating on how many edges should be removed at least to make an arbitrary node in k-core collapse. Therefore, in this paper, we make the first attempt to study the Targeted k-node Collapse Problem (TNCP) with four novel contributions. Firstly, we offer the general definition of TNCP problem with the proof of its NP-hardness. Secondly, in order to address the TNCP problem, we propose a heuristic algorithm named TNC and its improved version named ATNC for implementations on large-scale networks. After that, the experiments on 16 real-world networks across various domains verify the superiority of our proposed algorithms over 4 baseline methods along with detailed comparisons and analyses. Finally, the significance of TNCP problem for precisely evaluating the resilience of k-core structures in networks is validated.