An overview and perspective on social network monitoring

ABSTRACT

In this expository article we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and public health surveillance. We encourage researchers in the industrial process monitoring area to work on developing and comparing the performance of social network monitoring methods. We also discuss some of the issues in social network monitoring and give a number of research ideas.

KEYWORDS:

• Bayesian process monitoring
• scan methods
• social network analysis
• social network change detection
• statistical process monitoring

Funding

The work of W. H. Woodall was partially supported by National Science Foundation Grant CMMI-1436365.

Additional information

Notes on contributors

William H. Woodall

William H. Woodall is a Professor in the Department of Statistics at Virginia Tech. He is a former editor of the Journal of Quality Technology (2001–2003) and Associate Editor of Technometrics (1987–1995). He is a recipient of the Box Medal (2012), Shewhart Medal (2002), Jack Youden Prize (1995, 2003), Brumbaugh Award (2000, 2006), Søren Bisgaard Award (2012), Ellis Ott Foundation Award (1987), Lloyd S. Nelson Award (2014), and best paper award for IIE Transactions on Quality and Reliability Engineering (1997). He is a Fellow of the American Statistical Association, a Fellow of the American Society for Quality, and an elected member of the International Statistical Institute.

Meng J. Zhao

Meng J. Zhao is currently a Ph.D. candidate in the Department of Statistics at Virginia Tech. He is expected to receive his doctorate in statistics in 2017. He received an M.S. in statistics in the Department of Statistics at Virginia Tech and a M.S. in biotechnology at Pennsylvania State University. His Ph.D. research focuses in areas of statistical process monitoring and social network research. Prior to pursing a Ph.D. in statistics, he worked as a biochemist in the pharmaceutical industry for 4 years. He is a student member of the American Statistical Association.

Kamran Paynabar

Kamran Paynabar is an Assistant Professor in the H. Milton Stewart School of Industrial & Systems Engineering at the Georgia Institute of Technology. He received a B.Sc. and M.Sc. in Industrial Engineering from Iran University of Science and Technology and Azad University in 2002 and 2004, respectively, and a Ph.D. in Industrial and Operations Engineering from the University of Michigan in 2012. He holds an M.A. in Statistics from the University of Michigan. His research interests include high-dimensional data analysis for systems monitoring, diagnostics and prognostics, and statistical and machine learning for complex-structured streaming data including multi-stream signals, images, videos, point clouds, and network data. He is interested in applications ranging from manufacturing, including automotive and aerospace, to healthcare. He is a member of the Institute of Industrial Engineers and the Institute for Operations Research and the Management Sciences.

Ross Sparks

Ross S. Sparks is a Team Leader of Real-time Modelling and Monitoring at Data61, CSIRO, Australia. He holds a B.Sc. with majors in Mathematics and Mathematical Statistics from the University of Natal (1972), a B.Sc. (Hons, 1978) and M.Sc. (1980) from the University of South Africa, and a Ph.D. (1984) in Statistics from the University of Natal. His research interests include statistical quality control and improvement, all aspects of process monitoring, prospective public health surveillance, applied multivariate analysis, and applied statistics. He lectured at the University of Natal (1980–1983), University of Cape Town (1983–1988), and University of Wollongong (1988–1991) before joining CSIRO, Australia, in 1991. Since 1991 he has worked at CSIRO on applied research projects for industry.

James D. Wilson

James D. Wilson is an Assistant Professor of Statistics in the Department of Mathematics and Statistics at the University of San Francisco. He holds an M.S. in Mathematical Sciences from Clemson University (2010) and a Ph.D. in Statistics and Operations Research from the University of North Carolina at Chapel Hill (2015). His research brings together techniques from machine learning, statistical inference, and random graph theory to model, analyze, and explore relational (network) data structures. His interdisciplinary work has led to collaborations with researchers from a variety of fields, including genetics, infectious disease, political science, and managerial science.