A Fast Algorithm for Moderating Critical Nodes via Edge Removal

TL;DR

This paper introduces fast approximation algorithms for removing a limited number of edges to reduce a target node's influence in a network, addressing computational challenges in network moderation.

Contribution

It presents novel greedy algorithms with theoretical guarantees for minimizing information centrality via edge removal, including a nearly linear time method.

Findings

Algorithms significantly reduce target node influence in large networks.

Proposed methods are computationally efficient and scalable.

Experimental results validate effectiveness on real and synthetic networks.

Abstract

Critical nodes in networks are extremely vulnerable to malicious attacks to trigger negative cascading events such as the spread of misinformation and diseases. Therefore, effective moderation of critical nodes is very vital for mitigating the potential damages caused by such malicious diffusions. The current moderation methods are computationally expensive. Furthermore, they disregard the fundamental metric of information centrality, which measures the dissemination power of nodes. We investigate the problem of removing $k$ edges from a network to minimize the information centrality of a target node $\lea$ while preserving the network's connectivity. We prove that this problem is computationally challenging: it is NP-complete and its objective function is not supermodular. However, we propose three approximation greedy algorithms using novel techniques such as random walk-based Schur complement approximation and fast sum estimation. One of our algorithms runs in nearly linear time in the number of edges. To complement our theoretical analysis, we conduct a comprehensive set of experiments on synthetic and real networks with over one million nodes. Across various settings, the experimental results illustrate the effectiveness and efficiency of our proposed algorithms.