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Anomaly Detection in Static Networks using Egonets

Series: Department Seminar

Speaker: Dr. Srijan Sengupta Virginia Tech

Date/Time: Jan 13 16:00:00

Location: CSA Seminar Hall (Room No. 254, First Floor)

Faculty Advisor:

Anomaly in networks refers to the situation where the networked system, or part of it, shows significant departure from regular or expected behavioral patterns. Anomalies in networks often imply illegal or disruptive activity by the actors in the network. There has been a lot of recent emphasis on developing network monitoring tools that can detect such anomalous activity. Networks can be static, where we have a single snapshot of the system, or dynamic, where we have network snapshots at several points in time. Anomalies can have different meanings in these two scenarios. In static networks, anomaly typically means a local anomaly, in the form of a small anomalous subgraph which is significantly different from the rest of the network. Local anomalies are difficult to detect using simple network-level metrics since the anomalous subnetwork might be too small to cause significant changes to network-level metrics, e.g., network degree. Instead, such anomalies might be detectable if we monitor sub-network level metrics, e.g., degrees of all subgraphs. However, that option is computationally infeasible, as it involves computing total degrees for all O(2^n) subgraphs of an n-node network. We propose a novel anomaly detection method by using egonet p-values, where the egonet of a node is defined as the sub-network spanned by all neighbors of that node. Since there are exactly n egonets, the number of subgraphs being monitored is n, which is a relatively manageable number. We establish theoretical properties of the egonet method. We demonstrate its accuracy from simulation studies involving a broad range of statistical network models. We also illustrate the method on several well-studied network datasets

Speaker Bio:
Dr. Sengupta, received a bachelor and masters from the Indian Statistical Institute, Kolkota, and Ph.D. from University of Illinois at Urbana-Champaign. At present he is an Assistant Professor at Virginia Tech. His research interests are in Statistical methodology, Network data, Community structure in networks, Bootstrap and related resampling methods, Machine learning, Big data and computational statistics, Time series and Spatial data, Statistical applications in scientific fields.

Host Faculty: Prof. Ambedkar Dukkipati