{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:19:26Z","timestamp":1760239166489,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872066"],"award-info":[{"award-number":["61872066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20205895","type":"journal-article","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T21:26:06Z","timestamp":1603056366000},"page":"5895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network"],"prefix":"10.3390","volume":"20","author":[{"given":"Jiansu","family":"Pu","sequence":"first","affiliation":[{"name":"Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[{"name":"Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunbo","family":"Rao","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Eskin, E., Arnold, A., Prerau, M., Portnoy, L., and Stolfo, S. 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