A comparative analysis of approaches to network-dismantling

Sebastian Wandelt,Massimiliano Zanin,Daozhong Feng,Shlomo Havlin,Xiaoqian Sun

doi:10.1038/s41598-018-31902-8

Abstract

Estimating, understanding, and improving the robustness of networks has many application areas such as bioinformatics, transportation, or computational linguistics. Accordingly, with the rise of network science for modeling complex systems, many methods for robustness estimation and network dismantling have been developed and applied to real-world problems. The state-of-the-art in this field is quite fuzzy, as results are published in various domain-specific venues and using different datasets. In this study, we report, to the best of our knowledge, on the analysis of the largest benchmark regarding network dismantling. We reimplemented and compared 13 competitors on 12 types of random networks, including ER, BA, and WS, with different network generation parameters. We find that network metrics, proposed more than 20 years ago, are often non-dominating competitors, while many recently proposed techniques perform well only on specific network types. Besides the solution quality, we also investigate the execution time. Moreover, we analyze the similarity of competitors, as induced by their node rankings. We compare and validate our results on real-world networks. Our study is aimed to be a reference for selecting a network dismantling method for a given network, considering accuracy requirements and run time constraints.

Highlights

During the last decades, empirical studies have characterized a plethora of real-world systems through the complex network perspective[1,2], including air transport[3,4,5,6,7], power grids[8,9], the Internet backbone[10,11], inter-bank[12], or inter-personal networks[13]
Research on connectivity robustness has been performed in various scientific disciplines, the most important ones including complex network theory, bioinformatics, transportation/logistics and communication
Other related works rely on standard network metrics and their variants, including degree, k-shell decomposition[30], betweenness[31], and approximate betweenness[32]

Summary

Introduction

Empirical studies have characterized a plethora of real-world systems through the complex network perspective[1,2], including air transport[3,4,5,6,7], power grids[8,9], the Internet backbone[10,11], inter-bank[12], or inter-personal networks[13]. Wide-ranging network failures include the European air traffic disruption caused by the Icelandic Eyjafallajökull volcano eruption[14], large-scale power outages in the United States[15], computer virus spreading[16], or the cross-continental supply-chain shortages in the Japanese 2011 tsunami aftermath[17], and others[18]. In all these events, the affected countries had to face extremely high economic costs[19]. As a consequence of the heterogeneity of approaches and problems, the lack of common benchmarks, and the dispersal of research in different communities, today it is hardly possible to choose the best algorithm for a given problem

Methods

Results

Conclusion