Dismantling Networks by Skeleton Extraction and Greedy Tree Breaking

Xiaobin Rui,Fanrong Meng,Philip S Yu,Yahui Chai,Zhixiao Wang

doi:10.1109/access.2021.3086099

Abstract

Network dismantling is one of the important NP-hard problems in the field of social network analysis. It aims to break down networks into many small components of limited size by only removing a small group of nodes. One feasible way is to decycle (eliminating all the cycles) the network first and then break the acyclic graph. However, existing decycling-based algorithms mainly concentrate on the decycling step, ignoring the importance of the tree breaking process. Besides, none of the algorithms try to pre-process the network, which may bring improvement in both effectiveness and efficiency. In this paper, we fill these two gaps by proposing a novel network dismantling algorithm that combines skeleton extraction and greedy tree breaking (SEGTB). Network skeleton supports the whole network structure, whose removal would leave a much looser structure and serves as an effective pre-processing for the dismantling problem. The presented tree breaking method is provided with theoretical proofs on its lower bound. Experiments on ten real-world datasets show that our proposed SEGTB algorithm is both effective and efficient, outperforming the state-of-the-art.

Highlights

Network dismantling problem is one of the fundamental topology-related problems in the field of social network analysis [1]
To fill the above two gaps, i.e. ignoring the importance of the tree breaking step and missing the data pre-processing of the network, a novel network dismantling algorithm based on skeleton extraction and greedy tree breaking (SEGTB) is proposed in this paper
Just as the human bone skeleton could support our tissues and organs, the network skeleton props up the whole network structure

Summary

INTRODUCTION

Network dismantling problem is one of the fundamental topology-related problems in the field of social network analysis [1] It aims to find the minimal set of nodes whose removal could leave the remaining network broken into many disconnected small components. Existing graph-level algorithms mainly focus on the decycling process, ignore the importance of the tree breaking step None of these algorithms try to pre-process the network, which could improve both the performance and the running time simultaneously. To fill the above two gaps, i.e. ignoring the importance of the tree breaking step and missing the data pre-processing of the network, a novel network dismantling algorithm based on skeleton extraction and greedy tree breaking (SEGTB) is proposed in this paper.

RELATED WORKS

Findings

CONCLUSION AND FUTURE WORKS