ESND: An Embedding-based Framework for Signed Network Dismantling

TL;DR

This paper introduces ESND, an embedding-based framework for dismantling signed networks by considering the nature of relationships, demonstrating superior performance and stability across multiple datasets and analyzing the impact of sign proportions on network robustness.

Contribution

The paper presents a novel embedding-based algorithm, ESND, specifically designed for signed network dismantling, addressing the limitations of existing methods for unsigned networks.

Findings

ESND outperforms baseline methods in experiments.

Networks with more negative edges are easier to dismantle.

ESND maintains stable performance across different network structures.

Abstract

Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.