ADMIRING: Adversarial multi-network mining

Qinghai Zhou, Liangyue Li, Nan Cao, Lei Ying, Hanghang Tong

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

Abstract

Multi-sourced networks naturally appear in many application domains, ranging from bioinformatics, social networks, neuroscience to management. Although state-of-the-art offers rich models and algorithms to find various patterns when input networks are given, it has largely remained nascent on how vulnerable the mining results are due to the adversarial attacks. In this paper, we address the problem of attacking multi-network mining through the way of deliberately perturbing the networks to alter the mining results. The key idea of the proposed method (Admiring) is effective influence functions on the Sylvester equation defined over the input networks, which plays a central and unifying role in various multi-network mining tasks. The proposed algorithms bear two main advantages, including (1) effectiveness, being able to accurately quantify the rate of change of the mining results in response to attacks; and (2) generality, being applicable to a variety of multi-network mining tasks ( e.g., graph kernel, network alignment, cross-network node similarity) with different attacking strategies (e.g., edge/node removal, attribute alteration).

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1522-1527
Number of pages6
ISBN (Electronic)9781728146034
DOIs
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2019-November
ISSN (Print)1550-4786

Conference

Conference19th IEEE International Conference on Data Mining, ICDM 2019
CountryChina
CityBeijing
Period11/8/1911/11/19

Keywords

  • Adversarial attacks
  • Multi network mining
  • Sylvester Equation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Zhou, Q., Li, L., Cao, N., Ying, L., & Tong, H. (2019). ADMIRING: Adversarial multi-network mining. In J. Wang, K. Shim, & X. Wu (Eds.), Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019 (pp. 1522-1527). [8970779] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2019.00201