SA
Selin Aviyente leads the NSF project to
develop a new computational
framework for multilayer network
community detection.

Dr. Selin Aviyente, Professor of the Department of Electrical and Computer Engineering, and Dr. Tapabrata Maiti, Professor of the Department of Statistics & Probability received a three-year, $506,510 grant from the National Science Foundation. Congratulations!

This project entitled "CIF: Small: Community Detection in Multilayer Networks with Applications to Functional Connectivity Brain Networks” aims to addresses the problem of community detection in multilayer networks through three research thrusts. First, novel normalized-cut based quality functions will be defined for temporal, multiplex and multilayer networks, and computationally efficient algorithms will be developed to optimize these new cost functions. The convergence and consistency of the resulting algorithms will be studied. Next, generalized stochastic block models for temporal, multiplex and multilayer networks will be developed. Connections between maximizing a posteriori probabilities derived from these models and optimizing the heuristic quality functions will be established. Finally, the new community detection methods will be applied to multilayer functional connectivity networks, e.g. temporal and multi-frequency networks, constructed from electroencephalogram (EEG) data to assess well-known task-related networks. This new computational framework for multilayer network community detection can be applied to different types of networks including social, biological and ecological networks; we expect an impact on the fields of brain connectomics and cognitive neuroscience through collaborations with neuroscientists at Michigan State University. As part of the project, a diverse group of interdisciplinary researchers will be trained, and K-12 outreach activities that seek to engage female students will be organized.

An abstract of the project is available at https://nsf.gov/awardsearch/showAward?AWD_ID=2006800&HistoricalAwards=false.