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egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

1
Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
2
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5895; https://doi.org/10.3390/s20205895
Received: 1 September 2020 / Revised: 13 October 2020 / Accepted: 14 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)
The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks. View Full-Text
Keywords: anomaly detection; visualization; social communication; egocentric network; internet of things anomaly detection; visualization; social communication; egocentric network; internet of things
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MDPI and ACS Style

Pu, J.; Zhang, J.; Shao, H.; Zhang, T.; Rao, Y. egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network. Sensors 2020, 20, 5895.

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