Networks have emerged as one of the most powerful representation for modern complex data. They are ubiquitous in diverse areas of science and engineering ranging from social networks, communication networks to biological networks. Our lab focus on two general areas of network analysis: (1) Bayesian modeling of networks; (2) employing algebro-topological tools for network analysis.
Bao, B., You, K. and Lin, L. (2018). Network distance based on Laplacian flows on graphs. Link.
Paez, M., Amini, A. and Lin, L. (2018). Hierachical stochastic block model for community detection of multiplex networks. Preprint.
Kolaczyk, K., Lin, L., Rosenberg, S. and Walters, J. (2017). Averages of Unlabeled Networks: Geometric Characterization and Asymptotic Behavior. Submitted. Link.
Mukherjee, S. S., Sarkar, P., and Lin, L (2017). On clustering network-valued data. Neural Information Processing Systems 2017. Link.