@inproceedings{527fa31a35564e7084bd2367853d08e8,
title = "Attacking Similarity-Based Sign Prediction",
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. ",
keywords = "complexity, link prediction, networks, np-hardness, sign prediction, similarity measures",
author = "Godziszewski, {Michal Tomasz} and Michalak, {Tomasz P.} and Marcin Waniek and Talal Rahwan and Kai Zhou and Yulin Zhu",
note = "Funding Information: ACKNOWLEDGEMENTS Micha{\l} Tomasz Godziszewski and Tomasz Michalak were supported by the Polish National Science Centre grant 2016/23/B/ST6/03599. K. Zhou and Y. Zhu were supported by the PolyU Internal Fund (No. BE3U). Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Conference on Data Mining, ICDM 2021 ; Conference date: 07-12-2021 Through 10-12-2021",
year = "2021",
doi = "10.1109/ICDM51629.2021.00173",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1072--1077",
editor = "James Bailey and Pauli Miettinen and Koh, {Yun Sing} and Dacheng Tao and Xindong Wu",
booktitle = "Proceedings - 21st IEEE International Conference on Data Mining, ICDM 2021",
}