A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks

Huan Wang; Chunming Qiao

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A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks

机译：A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks

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Recently dynamic social networks witnessed a massive surge in popularity, especially in the area of anomaly detection. Although the text-based methods have achieved impressive detection performances, their applications are limited to the social text provided by users. This research focuses on graph-based methods and proposes a universal method for generalized social networks. Different from the existing graph-based methods that summarize a number of structural features, the proposed nodes' evolution diversity inspired method (&italic&NEDM&/italic&) detects anomalies in dynamic social networks from the perspective of diverse evolution mechanisms. More specifically, &italic&NEDM&/italic& applies link prediction algorithms at the micro-level to fit evolution mechanisms followed by the behaviors of nodes, and designs indices to evaluate their fitting degrees in edge removal and generation processes. In addition, the behavior of a node is represented as a quantum superposition state where such behavior follows different evolution mechanisms with uncertain probabilities. We propose a quantum mechanism based particle swarm optimization algorithm (&italic&QMPSO&/italic&) in &italic&NEDM&/italic&. &italic&QMPSO&/italic& determines the optimal observation states of the behaviors of different nodes, and maximally reflects the evolutional fluctuations in the evolution processes of social networks. As a result, &italic&NEDM&/italic& can quantify the evolutional fluctuations in different periods, and detect anomalies in dynamic social networks. Comparing with art-of-the-state methods and real social data in extensive experiments on disparate real-world social networks, we verify the outstanding performance of &italic&NEDM&/italic& in terms of both accuracy and universality.

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《IEEE Transactions on Knowledge and Data Engineering》 |2020年第10期|1868-1880|共13页
作者
Huan Wang; Chunming Qiao;
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作者单位

College of Informatics, Huazhong Agricultural University, Wuhan, China;

Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA;

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原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
Social networking (online); Evolution (biology); Heuristic algorithms; Cultural differences; Prediction algorithms; Birds; Anomaly detection; dynamic social network; link prediction; particle swarm optimization;

A Nodes' Evolution Diversity Inspired Method to Detect Anomalies in Dynamic Social Networks

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