Detection of anomalous meetings in a social network

Abstract-- When monitoring interactions within a social network, meetings or contacts between different members of the network are recorded. This paper addresses the problem of using the recorded meetings to determine (a) whether each meeting is anomalous and (b) the degree to which each meeting is anomalous. Performing robust statistical analysis on such data is particularly challenging when the number of observed meetings is much smaller than the number of people in the network. Our novel approach to anomaly detection in this high-dimensional setting is based on hypergraphs, an important extension of graphs which allows edges to connect more than two vertices simultaneously. In particular, the distribution of meetings is modeled as a two-component mixture of a "nominal" distribution and a distribution of anomalous events. A variational ExpectationMaximization algorithm is then used to assess the likelihood of each observation being anomalous. The computational complexity of...

• Type: Conference
• Year: 2008
• Where: CISS
• Authors: Jorge Silva, Rebecca Willett
• Keywords: CISS 2008, Information Technology, Meetings, Robust Statistical Analysis, Social Network