Visual Analytic of Big Data Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sensors (Basel). 2020 Oct 18;20(20):5895. doi: 10.3390/s20205895.
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.
互联网的发展使得社会交流对于维持人际关系变得日益重要。然而，广告和欺诈也在飞速增长，严重影响了我们的日常生活，例如导致金钱和时间的损失、垃圾信息和隐私问题。因此，检测社交网络中的异常现象非常重要。然而，现有的异常检测方法不能保证准确率。此外，由于缺乏标记数据，我们也不能直接使用检测结果。换句话说，我们仍然需要人类分析师参与其中，为决策提供足够的判断。为了帮助专家更客观、有效地分析和探索社交网络中异常检测的结果，我们提出了一种新颖的可视化系统 egoDetect，它可以有效地检测社交通信网络中的异常。该系统基于无监督异常检测方法，可以在不进行训练的情况下进行异常检测，并快速获得概览。然后，我们通过设计一种基于以自我为中心的网络图的新颖图符来探索自我的拓扑结构以及自我与改变者之间的关系。此外，它还为专家提供了丰富的交互，以便快速导航到感兴趣的用户进行进一步的探索。我们使用运营商提供的实际呼叫数据集来评估我们的系统。结果证明，我们提出的系统在社交网络的异常检测中是有效的。
