Complex network theory has proved to be a versatile framework to represent and analyze relational data that is abundant in many disciplines including the social sciences, information systems, biology and neuroscience. Until recently, the research on network theory has mainly focused on static graphs, i.e. the relationships between nodes of the network do not change with time. However, almost all real networks are dynamic in nature such as social networks with connections that change over time or functional neural networks that reorganize rapidly in response to stimuli. Most of the current studies of dynamic networks focuses on two major and interrelated problems: anomaly or change point detection in a time series of graphs and evolutionary clustering to identify the time-varying structure of networks. This research addresses these two problems simultaneously in a unified framework to monitor and track the changes in network topology and to characterize each ?network state? with a single community structure. In particular, this research focuses on the functional connectivity networks (FCNs) of the human brain that reorganize themselves dynamically during perception, cognition and execution of mental processes.
The investigator develops two complementary approaches to address dynamic network monitoring problem: 1) A multi-scale framework for joint anomaly detection and network state identification in time-varying networks; and, 2) Consensus spectral clustering methods along with tensor decomposition for succinct topographic representation of network states. Finally, this dynamic network monitoring framework is applied to electroencephalogram (EEG) data collected using an experimental protocol designed to assess well-known salience and control functional networks associated with affective regulation and cognitive control.
