Can the structure of a network reveal how it is formed? Community Detection or graph clustering is an important field in modern data science, that extracts useful information from the structure of social networks, citation networks, etc. In recent years, the field has seen important theoretical breakthroughs via the development of algorithms with provable performance guarantees on random graphs. This talk develops on this theme with an exposition of a hitherto unknown random graph model that better represents real world networks. A study of message passing algorithms for community recovery is also presented.
Suryanarayana Sankagiri is a Ph.D. student in Electrical and Computer Engineering at the University of Illinois, Urbana Champaign. He obtained his B.Tech. from IIT Bombay in 2016, and M.S. from the University of Illinois in 2018. His research interests lie primarily in the modeling and analysis of engineering systems as random processes with a special focus on blockchains. Beyond research, he enjoys teaching as well as outdoor activities like running, biking and bird watching