Abstract

It is important to understand the behavior within a social network, particularly excessive communications between nodes. Such excessive activities in a network provide an insight into the pattern of communication between nodes, which, in some cases, could lead to a fraudulent behavior. Scan statistics have been applied before to detect the excessive communications in email networks. However, they alone are not effective in revealing the dynamic relationships and progression of chatter as the scan statistics relate to the maximum of locality statistics. Here a multivariate time series model, vector autoregressive (VAR) model, has been developed and applied to the metadata of organization e-mails as a case study to detect a group of influential nodes and their dynamic relationship. Furthermore, we devise a new methodology, which utilizes the probabilistic topic model obtained from the e-mail content, scan statistics, and time series of maximum information flow. We demonstrate how the influential vertices obtained from the VAR model are connected with the anomalous topic activities. These methodologies would be highly useful in studying the excessive communications and anomalous topic activities in other dynamic networks, such as, twitter networks, telephone calls, scientific collaborations, and other social networks.

摘要

了解社交网络中的行为非常重要，尤其是节点之间的过度通信。网络中的这种过度活动提供了对节点之间通信模式的洞察，在某些情况下，这可能导致欺诈行为。之前已应用扫描统计信息来检测电子邮件网络中的过度通信。然而，它们本身并不能有效地揭示动态关系和颤动的进展，因为扫描统计与局部统计的最大值相关。这里开发了一个多元时间序列模型，向量自回归 (VAR) 模型，并将其作为案例研究应用于组织电子邮件的元数据，以检测一组有影响的节点及其动态关系。此外，我们设计了一种新的方法，它利用了从电子邮件内容、扫描统计和最大信息流时间序列中获得的概率主题模型。我们展示了从 VAR 模型中获得的有影响力的顶点如何与异常主题活动相关联。这些方法对于研究其他动态网络中的过度通信和异常主题活动非常有用，例如推特网络、电话、科学合作和其他社交网络。