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The value of summary statistics for anomaly detection in temporally evolving networks: A performance evaluation study
Author(s) -
Kodali Lata,
Sengupta Srijan,
House Leanna,
Woodall William H.
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2548
Subject(s) - anomaly detection , snapshot (computer storage) , computer science , autocorrelation , anomaly (physics) , data mining , statistics , summary statistics , focus (optics) , data science , mathematics , physics , optics , condensed matter physics , operating system
Analysis of 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 the use 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 autocorrelation 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 the use of summary statistics can be valuable tools for network monitoring and often perform better than more complicated statistics.