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DTEND:20200113T120000Z
UID:f2e2a1e66576d2296d0ecf7669c6ebb9-46
DTSTAMP:19700101T120016Z
DESCRIPTION:Anomaly Detection in Static Networks using Egonets
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/46/anomaly-detection-in-static-networks-using-egonets/
SUMMARY: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
DTSTART:20200113T120000Z
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