Title: Forest Expression and Online Monitoring of Dynamic Networks

Abstract:

Network sequences are widely used to describe the longitudinal evolution of dynamic systems. Effective online monitoring of such sequences is crucial for detecting temporal structural changes in these systems. In the statistical process control (SPC) literature, a common approach is to extract key features from observed networks and then apply an SPC chart to monitor these features sequentially over time. However, existing methods often rely on features that are insensitive to certain important structural changes, and the control charts employed may not adequately capture the complex dependence structure among the extracted features. In our recent research, we propose four specific features to characterize the structure of an observed network. Collectively, these features can capture most of the structural changes of interest across various applications. After extracting these features, we employ a multivariate nonparametric control chart for online monitoring. Furthermore, we introduce a novel framework that represents each connected component of a network as a tree and the entire network as a forest. This forest-based representation enables intuitive 3D visualization and provides new structural features that enhance network comparison and monitoring. These new methods for network visualization and monitoring will be discussed in this talk, accompanied by extensive numerical demonstrations.

Bio:

Dr. Peihua Qiu is the Dean’s Professor and Founding Chair of the Department of Biostatistics at the University of Florida. He earned his PhD in Statistics from the University of Wisconsin–Madison in 1996. Dr. Qiu has made major contributions to several research areas, including jump regression analysis, image processing, statistical process control, survival analysis, dynamic disease screening, and spatio-temporal disease surveillance. He is the author of three books and more than 180 peer-reviewed journal articles. Dr. Qiu is an elected Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the American Society for Quality (ASQ), and the Institute of Mathematical Statistics (IMS), and an elected member of the International Statistical Institute (ISI). He previously served as Editor of Technometrics (2014–2016) and is the 2024 recipient of the Shewhart Medal.