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arXiv · 1910.06497

The Value of Summary Statistics for Anomaly Detection in Temporally-Evolving Networks: A Performance Evaluation Study

Lata Kodali·Srijan Sengupta·Leanna House·William H. Woodall
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Abstract

Network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated historical data unchanging over time. However, most networks result from temporally-evolving systems that exhibit intrinsic dynamic behavior. Monitoring such temporally-varying networks to detect anomalous changes has applications in both social and physical sciences. In this work, we perform an evaluation study of summary statistics for anomaly detection in temporally-evolving networks by incorporating principles from statistical process monitoring. In contrast to previous studies, we deliberately incorporate temporal auto-correlation in our study. Other considerations in our comprehensive assessment include types and duration of anomaly, model type, and sparsity in temporally-evolving networks. We conclude that summary statistics can be valuable tools for network monitoring and often perform better than more complicated statistics.

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Lata Kodali, Srijan Sengupta, Leanna House, William H. Woodall. 2019-10-15. The Value of Summary Statistics for Anomaly Detection in Temporally-Evolving Networks: A Performance Evaluation Study. https://arxiv.org/abs/1910.06497

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