Paper Info
Title
Admiring: Adversarial Multi-Network Mining
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).
Year
DOI
Venue
2019
10.1109/ICDM.2019.00201
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019)
Field
DocType
ISSN
Graph kernel,Social network,Sylvester equation,Network mining,Computer science,Network alignment,Ranging,Artificial intelligence,Machine learning,Generality,Adversarial system
Conference
1550-4786
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Qinghai Zhou191.97
Liangyue Li213710.68
Nan Cao375952.57
Lei Ying491170.96
Hanghang Tong53560202.37