research-article

R-energy for evaluating robustness of dynamic networks

Authors:

David LoAuthors Info & Claims

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

Pages 89 - 98

https://doi.org/10.1145/2464464.2464486

Published: 02 May 2013 Publication History

Abstract

The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves, its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O(|V| + |E|) where V and E are the vertex set and edge set of the network respectively. This makes R-energy more efficient to compute than algebraic connectivity, another well known network robustness measure. Our experiments also show that removal of high degree vertices reduces network robustness (measured by R-energy) more than that of random or small degree vertices. R-energy can scale well for very large networks. It takes as little as 40 seconds to compute for a network with about 5M vertices and 69M edges. We can further detect events occurring in a dynamic Twitter network with about 130K users and discover interesting weekly tweeting trends by tracking changes to R-energy.

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Cited By

Kumar RKumari SMishra A(2023)Robustness of multilayer networks: A graph energy perspectivePhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.129160628(129160)Online publication date: Oct-2023
https://doi.org/10.1016/j.physa.2023.129160
Zheng JGao MLim ELo DJin CZhou A(2022)On measuring network robustness for weighted networksKnowledge and Information Systems10.1007/s10115-022-01670-z64:7(1967-1996)Online publication date: 1-Jul-2022
https://doi.org/10.1007/s10115-022-01670-z
Marzo JCalle ECosgaya SRueda DManosa A(2018)On Selecting the Relevant Metrics of Network Robustness2018 10th International Workshop on Resilient Networks Design and Modeling (RNDM)10.1109/RNDM.2018.8489809(1-7)Online publication date: Aug-2018
https://doi.org/10.1109/RNDM.2018.8489809
Show More Cited By

Index Terms

R-energy for evaluating robustness of dynamic networks
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Published In

cover image ACM Conferences

WebSci '13: Proceedings of the 5th Annual ACM Web Science Conference

May 2013

481 pages

ISBN:9781450318891

DOI:10.1145/2464464

Conference Chairs:
Hugh Davis
University of Southampton
,
Harry Halpin
World Wide Web Consortium
,
Alex Pentland,
Program Chairs:
Mark Bernstein,
Lada Adamic,
Harith Alani,
Alexandre Monnin,
Richard Rogers

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 02 May 2013

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National Research Foundation-Prime Minister's office, Republic of Singapore

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WebSci '13

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SIGWEB

WebSci '13: Web Science 2013

May 2 - 4, 2013

Paris, France

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Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

Kumar RKumari SMishra A(2023)Robustness of multilayer networks: A graph energy perspectivePhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.129160628(129160)Online publication date: Oct-2023
https://doi.org/10.1016/j.physa.2023.129160
Zheng JGao MLim ELo DJin CZhou A(2022)On measuring network robustness for weighted networksKnowledge and Information Systems10.1007/s10115-022-01670-z64:7(1967-1996)Online publication date: 1-Jul-2022
https://doi.org/10.1007/s10115-022-01670-z
Marzo JCalle ECosgaya SRueda DManosa A(2018)On Selecting the Relevant Metrics of Network Robustness2018 10th International Workshop on Resilient Networks Design and Modeling (RNDM)10.1109/RNDM.2018.8489809(1-7)Online publication date: Aug-2018
https://doi.org/10.1109/RNDM.2018.8489809
He XGao MKan MWang D(2017)BiRankIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.261158429:1(57-71)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2611584
Zheng JLi YHou YGao MZhou A(2017)BMNR: Design and Implementation a Benchmark for Metrics of Network Robustness2017 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICBK.2017.58(320-325)Online publication date: Aug-2017
https://doi.org/10.1109/ICBK.2017.58

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