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

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 priva...

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Detalles Bibliográficos
Autores principales: Pu, Jiansu, Zhang, Jingwen, Shao, Hui, Zhang, Tingting, Rao, Yunbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589889/
https://www.ncbi.nlm.nih.gov/pubmed/33081065
http://dx.doi.org/10.3390/s20205895
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author Pu, Jiansu
Zhang, Jingwen
Shao, Hui
Zhang, Tingting
Rao, Yunbo
author_facet Pu, Jiansu
Zhang, Jingwen
Shao, Hui
Zhang, Tingting
Rao, Yunbo
author_sort Pu, Jiansu
collection PubMed
description 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.
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spelling pubmed-75898892020-10-29 egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network Pu, Jiansu Zhang, Jingwen Shao, Hui Zhang, Tingting Rao, Yunbo Sensors (Basel) Article 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. MDPI 2020-10-18 /pmc/articles/PMC7589889/ /pubmed/33081065 http://dx.doi.org/10.3390/s20205895 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pu, Jiansu
Zhang, Jingwen
Shao, Hui
Zhang, Tingting
Rao, Yunbo
egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title_full egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title_fullStr egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title_full_unstemmed egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title_short egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network
title_sort egodetect: visual detection and exploration of anomaly in social communication network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7589889/
https://www.ncbi.nlm.nih.gov/pubmed/33081065
http://dx.doi.org/10.3390/s20205895
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