Statistical evaluation of spectral methods for anomaly detection in static networks

The topic of anomaly detection in networks has attracted a lot of attention in recent years, especially with the rise of connected devices and social networks. Anomaly detection spans a wide range of applications, from detecting terrorist cells in counter-terrorism efforts to identifying unexpected mutations during ribonucleic acid transcription. Fittingly, numerous algorithmic techniques for anomaly detection have been introduced. However, to date, little work has been done to evaluate these algorithms from a statistical perspective. This work is aimed at addressing this gap in the literature by carrying out statistical evaluation of a suite of popular spectral methods for anomaly detection in networks. Our investigation on the statistical properties of these algorithms reveals several important and critical shortcomings that we make methodological improvements to address. Further, we carry out a performance evaluation of these algorithms using simulated networks and extend the methods from binary to count networks

Anomaly (physics · Anomaly detection · Binary number · Data mining · Machine learning · Suite · Anomaly Detection Techniques and Applications · Complex Network Analysis Techniques · Computer Science · Mathematics · Network Security and Intrusion Detection · Artificial Intelligence

• Anomaly detection in online social networks

  Open Access•David Savage, Xiuzhen Zhang et al.•Social Networks•2014

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