Attacking Similarity-Based Sign Prediction

Michal Tomasz Godziszewski, Tomasz P. Michalak, Marcin Waniek, Talal Rahwan, Kai Zhou, Yulin Zhu

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

Abstract

In this paper, we present a computational analysis of the problem of attacking sign prediction, whereby the aim of the attacker (a network member) is to hide from the defender (an analyst) the signs of a target set of links by removing the signs of some other, non-target, links. The problem turns out to be NP-hard if either local or global similarity measures are used for sign prediction. We propose a heuristic algorithm and test its effectiveness on several real-life and synthetic datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining, ICDM 2021
EditorsJames Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1072-1077
Number of pages6
ISBN (Electronic)9781665423984
DOIs
StatePublished - 2021
Event21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand
Duration: Dec 7 2021Dec 10 2021

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2021-December
ISSN (Print)1550-4786

Conference

Conference21st IEEE International Conference on Data Mining, ICDM 2021
Country/TerritoryNew Zealand
CityVirtual, Online
Period12/7/2112/10/21

Keywords

  • complexity
  • link prediction
  • networks
  • np-hardness
  • sign prediction
  • similarity measures

ASJC Scopus subject areas

  • Engineering(all)

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