research-article

Correlation of Node Importance Measures: An Empirical Study through Graph Robustness

Authors:

Mirza Basim Baig,

Leman AkogluAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 275 - 281

https://doi.org/10.1145/2740908.2743055

Published: 18 May 2015 Publication History

Abstract

Graph robustness is a measure of resilience to failures and targeted attacks. A large body of research on robustness focuses on how to attack a given network by deleting a few nodes so as to maximally disrupt its connectedness. As a result, literature contains a myriad of attack strategies that rank nodes by their relative importance for this task. How different are these strategies? Do they pick similar sets of target nodes, or do they differ significantly in their choices? In this paper, we perform the first large scale empirical correlation analysis of attack strategies, i.e., the node importance measures that they employ, for graph robustness. We approach this task in three ways; by analyzing similarities based on (i) their overall ranking of the nodes, (ii) the characteristics of top nodes that they pick, and (iii) the dynamics of disruption that they cause on the network. Our study of 15 different (randomized, local, distance-based, and spectral) strategies on 68 real-world networks reveals surprisingly high correlations among node-attack strategies, consistent across all three types of analysis, and identifies groups of comparable strategies. These findings suggest that some computationally complex strategies can be closely approximated by simpler ones, and a few strategies can be used as a close proxy of the consensus among all of them.

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Cited By

Freitas SYang DKumar STong HChau D(2022)Graph Vulnerability and Robustness: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3163672(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3163672
Logins ALi YKarras P(2022)On the Robustness of Diffusion in a Network Under Node AttacksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.307108134:12(5884-5895)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3071081
Chernikova AGozzi NBoboila SAngadi PLoughner JWilden MPerra NEliassi-Rad TOprea A(2022)Cyber Network Resilience Against Self-Propagating Malware AttacksComputer Security – ESORICS 202210.1007/978-3-031-17140-6_26(531-550)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-031-17140-6_26
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Correlation of Node Importance Measures: An Empirical Study through Graph Robustness

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Published In

cover image ACM Other conferences

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Army Research Office
National Science Foundation

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Freitas SYang DKumar STong HChau D(2022)Graph Vulnerability and Robustness: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3163672(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3163672
Logins ALi YKarras P(2022)On the Robustness of Diffusion in a Network Under Node AttacksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.307108134:12(5884-5895)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3071081
Chernikova AGozzi NBoboila SAngadi PLoughner JWilden MPerra NEliassi-Rad TOprea A(2022)Cyber Network Resilience Against Self-Propagating Malware AttacksComputer Security – ESORICS 202210.1007/978-3-031-17140-6_26(531-550)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-031-17140-6_26
Logins ALi YKarras P(2020)On the Robustness of Cascade Diffusion under Node AttacksProceedings of The Web Conference 202010.1145/3366423.3380028(2711-2717)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380028
Chen BHua YYuan YJin Y(2018)Link Prediction on Directed Networks Based on AUC OptimizationIEEE Access10.1109/ACCESS.2018.28382596(28122-28136)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2838259
Grando FNoble DLamb L(2016)An Analysis of Centrality Measures for Complex and Social Networks2016 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2016.7841580(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/GLOCOM.2016.7841580

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