research-article

Detecting and Assessing Anomalous Evolutionary Behaviors of Nodes in Evolving Social Networks

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 1

Article No.: 12, Pages 1 - 24

https://doi.org/10.1145/3299886

Published: 23 January 2019 Publication History

Abstract

Based on the performance of entire social networks, anomaly analysis for evolving social networks generally ignores the otherness of the evolutionary behaviors of different nodes, such that it is difficult to precisely identify the anomalous evolutionary behaviors of nodes (AEBN). Assuming that a node's evolutionary behavior that generates and removes edges normally follows stable evolutionary mechanisms, this study focuses on detecting and assessing AEBN, whose evolutionary mechanisms deviate from their past mechanisms, and proposes a link prediction detection (LPD) method and a matrix perturbation assessment (MPA) method. LPD describes a node's evolutionary behavior by fitting its evolutionary mechanism, and designs indexes for edge generation and removal to evaluate the extent to which the evolutionary mechanism of a node's evolutionary behavior can be fitted by a link prediction algorithm. Furthermore, it detects AEBN by quantifying the differences among behavior vectors that characterize the node's evolutionary behaviors in different periods. In addition, MPA considers AEBN as a perturbation of the social network structure, and quantifies the effect of AEBN on the social network structure based on matrix perturbation analysis. Extensive experiments on eight disparate real-world networks demonstrate that analyzing AEBN from the perspective of evolutionary mechanisms is important and beneficial.

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Detecting and Assessing Anomalous Evolutionary Behaviors of Nodes in Evolving Social Networks

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 1

February 2019

340 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3301280

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Publication History

Published: 23 January 2019

Accepted: 01 November 2018

Revised: 01 September 2018

Received: 01 March 2018

Published in TKDD Volume 13, Issue 1

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Collaborative Research Project (CRP) between Macquarie University and Data61 on dynamic graph mining
National Key Research and Development Program of China
Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China
National Natural Science Foundation of China
MQNS
MQ EPS

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https://dl.acm.org/doi/10.1145/3639408
Wei SWang LWu HZhou MLi QXiao Y(2024)Link prediction method for social networks based on a hierarchical and progressive user interaction matrixKnowledge-Based Systems10.1016/j.knosys.2024.111929297(111929)Online publication date: Aug-2024
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Wang HLiu RShi CChen JFang LLiu SGong Z(2023)Resisting the Edge-Type Disturbance for Link Prediction in Heterogeneous NetworksACM Transactions on Knowledge Discovery from Data10.1145/361409918:2(1-24)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3614099
Wang HCui ZLiu SNi QGong Z(2023)Evaluating Edge Credibility in Evolving Noisy Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322340335:11(11342-11353)Online publication date: 1-Nov-2023
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Ma XWu JXue SYang JZhou CSheng QXiong HAkoglu L(2023)A Comprehensive Survey on Graph Anomaly Detection With Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.311881535:12(12012-12038)Online publication date: 1-Dec-2023
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