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The Value of Summary Statistics for Anomaly Detection in Temporally-Evolving Networks: A Performance Evaluation Study by Kodali, Lata; Sengupta, Srijan; House, Leanna; Woodall, William H. is a scholarly article available to read on EtoBox.

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

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.

Author

Kodali, Lata; Sengupta, Srijan; House, Leanna; Woodall, William H.

Published

2019

Language

EN