Make it or break it: Manipulating robustness in large networks

Hau Chan, Leman Akoglu, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

The function and performance of networks rely on their robustness, defined as their ability to continue functioning in the face of damage (targeted attacks or random failures) to parts of the network. Prior research has proposed a variety of measures to quantify robustness and various manipulation strategies to alter it. In this paper, our contributions are twofold. First, we critically analyze various robustness measures and identify their strengths and weaknesses. Our analysis suggests natural connectivity, based on the weighted count of loops in a network, to be a reliable measure. Second, we propose the first principled manipulation algorithms that directly optimize this robustness measure, which lead to significant performance improvement over existing, ad-hoc heuristic solutions. Extensive experiments on real-world datasets demonstrate the effectiveness and scalability of our methods against a long list of competitor strategies.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
PublisherSociety for Industrial and Applied Mathematics Publications
Pages325-333
Number of pages9
Volume1
ISBN (Print)9781510811515
DOIs
StatePublished - 2014
Externally publishedYes
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Other

Other14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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