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Abstract:
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Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various areas of research like in neuroscience, where an important objective is the reconstruction of the dynamic manner of brain region interactions. Most of the statistical methods for detecting the anomalies have the following limitation: network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a new distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization (e.g. mean degree). Secondly, we adopt a change point detection method for (weakly) dependent time series based on efficient scores and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated data and to two fMRI data and find that our method delivers accurate and realistic results in terms of identifying true change points locations compared to the results reported by competing approaches.
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