Network structural perturbation against interlayer link prediction

TL;DR

This paper investigates how perturbing intralayer links in multiplex networks affects interlayer link prediction accuracy, proposing global and local strategies to identify critical links and analyzing their impact on prediction performance.

Contribution

It introduces novel network perturbation methods to analyze the importance of intralayer links in interlayer link prediction, highlighting the impact of small and large degree node links.

Findings

Intralayer links connected with small degree nodes significantly affect prediction accuracy.

Links connected with large degree nodes may negatively impact prediction.

Perturbation strategies reveal the backbone structures crucial for accurate interlayer link prediction.

Abstract

Interlayer link prediction aims at matching the same entities across different layers of the multiplex network. Existing studies attempt to predict more accurately, efficiently, or generically from the aspects of network structure, attribute characteristics, and their combination. Few of them analyze the effects of intralayer links. Namely, few works study the backbone structures which can effectively preserve the predictive accuracy while dealing with a smaller number of intralayer links. It can be used to investigate what types of intralayer links are most important for correct prediction. Are there any intralayer links whose presence leads to worse predictive performance than their absence, and how to attack the prediction algorithms at the minimum cost? To this end, two kinds of network structural perturbation methods are proposed. For the scenario where the structural information of the whole network is completely known, we offer a global perturbation strategy that gives different perturbation weights to different types of intralayer links and then selects a predetermined proportion of intralayer links to remove according to the weights. In contrast, if these information cannot be obtained at one time, we design a biased random walk procedure, local perturbation strategy, to execute perturbation. Four kinds of interlayer link prediction algorithms are carried out on different real-world and artificial perturbed multiplex networks. We find out that the intralayer links connected with small degree nodes have the most significant impact on the prediction accuracy. The intralayer links connected with large degree nodes may have side effects on the interlayer link prediction.