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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7589889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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