@inproceedings{4a2c6638e610472f933902d06fc92122,
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.",
year = "2021",
month = dec,
doi = "10.1109/ICDM51629.2021.00173",
language = "English",
isbn = "978-1-6654-2398-4",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "IEEE",
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",
address = "United States",
}