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Abstract
Network modeling and analysis has become a fundamental tool for studying various complex systems. This paper proposes an extension of statistical monitoring to network streams, which is crucial for effective decision-making in various applications. To this end, a model for the probability of edge existence as a function of vertex attributes is constructed and a likelihood method is developed to monitor the underlying network model. The method is flexible to detect any form of anomaly that arises from different network edge-formation mechanisms. Experiments on simulated and real network streams depict the properties and benefits of the method compared with existing methods in the literature.
Additional information
Notes on contributors
Bahareh Azarnoush
Dr. Azarnoush is Senior Data Scientist at Netflix. Her email is [email protected].
Kamran Paynabar
Dr. Paynabar is Assistant Professor at the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Institute of Technology. His email address is [email protected].
Jennifer Bekki
Dr. Bekki is Assistant Professor at the Department of Engineering and Computing Systems at Arizona State University. Her email address is [email protected].
George Runger
Dr. Runger is Chair of the Department of Biomedical Informatics at Arizona State University. His email address is [email protected].
