Modelling and Mining of Networked Information Spaces

Project Title: Anomaly Detection in Dynamic Communication Graphs
Participants: Xiaomeng Wan, Dr. Nauzer Kalyaniwalla, Dr. Jeannette Janssen, Dr. Evangelos Milios
Project Description: Communication networks can be modeled as dynamic graphs with time-varying edges. Real-life events may cause communications that are unusual in either volume or pattern. Event detection focuses on identifying these unusual patterns in communication graphs. It is crucial for counter terrorism, network surveillance and traffic management. Most event detection methods only focus on network-wide events. However, local events, that is those associated with only a few individuals, are more common and of significant interest. In this study, we develop an approach to detect those events with only local impacts. The difficulty of this task is that the networks usually consist of heterogeneous vertices representing people who play different roles in the community or organization. These vertices have different communication patterns and thus, different definitions of "anomaly". Methods treating them without discrimination would give very noisy results and are usually biased by a specific kind of vertices. In our approach, we define a set of metrics to characterize people's communications from different viewpoints, and cluster them into groups with similar behaviours. Deviations from previous behaviours and typical cluster behaviours are regarded as evidence of events or anomalies. This approach will be tested on both synthetic data and real email data such as the Enron dataset.