Detecting and assessing anomalous evolutionary behaviors of nodes in evolving social networks

Huan Wang, Jia Wu, Wenbin Hu, Xindong Wu

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.
Original languageEnglish
Article number12
Pages (from-to)1-24
Number of pages24
JournalACM Transactions on Knowledge Discovery from Data
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Anomalous evolutionary behaviors of nodes
  • assessment
  • detection
  • perturbation analysis
  • social network

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