talk: Community Detection in Multilayer Networks, 1-2pm 10/3

Modern data analysis and processing tasks typically involve large sets of structured data, where the structure carries critical information about the nature of the data.  Typically, graphs are used as mathematical tools to describe the structure of such data. Traditional network models employ simple graphs where the nodes are connected to each other by a single, static edge.  However, in many contemporary applications, this relatively simple structure cannot capture the diverse nature of the networks, e.g., multiple types of entities and interactions between them. Multilayer networks (MLNs) allow one to represent the interactions between a pair of nodes through multiple types of links. MLNs can further be categorized based on the homogeneity of the nodes and complexity of topological structure as: i) multiplex networks where each layer has the same set of entities of the same type and inter-layer edges are not shown as they are implicit; ii) heterogenous multilayer networks where the set and types of entities may be different for each layer and the relationships of entities across layers are shown using explicit inter-layer edges. A core task in the complexity reduction of these high-dimensional networks is community detection. In this talk, a joint nonnegative matrix factorization approach is proposed to detect the community structure in both multiplex and multilayer networks. The proposed approach considers the heterogeneity of layers and formulates community detection as a regularized optimization problem. Applications of the proposed approach for social and biological networks will be highlighted.

Selin Aviyente received her B.S. degree with high honors in Electrical and Electronics engineering from Bogazici University, Istanbul. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor. She joined the Department of Electrical and Computer Engineering at Michigan State University in 2002, where she is currently a Professor and Associate Chair for Undergraduate Studies. Her research focuses on statistical and nonstationary signal processing, higher-order data representations and network science with applications to neuronal signals. She has authored more than 150 peer-reviewed journal and conference papers. She is the recipient of a 2005 Withrow Teaching Excellence Award, a 2008 NSF CAREER Award and 2021 Withrow Excellence in Diversity Award. She is currently serving as the chair of IEEE Signal Processing Society Bioimaging and Signal Processing Technical Committee, on the Steering Committees of IEEE SPS Data Science Initiative and IEEE BRAIN. She has served as an Associate Editor and Senior Area Editor for IEEE Transactions on Signal Processing, IEEE Transactions on Signal and Information Processing over Networks, IEEE Open Journal of Signal Processing and Digital Signal Processing.

Host: Prof. Tulay Adali