Detection of excessive activities in time series of graphs.

ABSTRACT: Considerable efforts have been made to apply scan statistics in detecting fraudulent or excessive activities in dynamic email networks. However, previous studies are mostly based on the fixed and disjoint windows, and on the assumption of short-term stationarity of the series, which might result in loss of information and error in detecting excessive activities. Here we devise scan statistics with variable and overlapping windows on stationary time series of organizational emails with a two-step process, and use likelihood function to rank the clusters. We initially estimate the log-likelihood ratio to obtain a primary cluster of communications using the Poisson model on email count series, and then extract neighborhood ego subnetworks around the observed primary cluster to obtain more ref

SUBMITTER: Goswami S 

PROVIDER: S-EPMC9038063 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature