  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.
