Attacking similarity-based link prediction in social networks

Kai Zhou, Tomasz P. Michalak, Marcin Waniek, Talal Rahwan, Yevgeniy Vorobeychik

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Link prediction is one of the fundamental problems in computational social science. A particularly common means to predict existence of unobserved links is via structural similarity metrics, such as the number of common neighbors; node pairs with higher similarity are thus deemed more likely to be linked. However, a number of applications of link prediction, such as predicting links in gang or terrorist networks, are adversarial, with another party incentivized to minimize its effectiveness by manipulating observed information about the network. We offer a comprehensive algorithmic investigation of the problem of attacking similarity-based link prediction through link deletion, focusing on two broad classes of such approaches, one which uses only local information about target links, and another which uses global network information. While we show several variations of the general problem to be NP-Hard for both local and global metrics, we exhibit a number of well-motivated special cases which are tractable. Additionally, we provide principled and empirically effective algorithms for the intractable cases, in some cases proving worst-case approximation guarantees.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages305-313
Number of pages9
ISBN (Electronic)9781510892002
Publication statusPublished - 2019
Externally publishedYes
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume1
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
CountryCanada
CityMontreal
Period13/05/1917/05/19

Keywords

  • Adversarial attacks
  • Computational social science
  • Link prediction
  • Security and privacy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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