| Title | : | Recovering Temporal Information in Dynamic Networks |
| Speaker | : | Jithin K. Sreedharan (Purdue University, USA) |
| Details | : | Wed, 10 Apr, 2019 11:30 AM @ A M Turing Hall |
| Abstract: | : | Dynamic networks/graphs are the natural framework to represent data from many of the complex systems involving time-evolving interacting entities. The problem of reverse engineering the evolution of dynamic networks is one of profound importance in diverse application domains: in analysis of infection spread, it reveals the temporal processes underlying infection; in analysis of biomolecular interaction networks (e.g., protein interaction networks), it reveals early molecules that are known to be differentially implicated in diseases; in economic networks, it reveals flow of capital and associated actors. To this end, in this talk I will present my recent work on inferring node arrival order -- for a dynamic graph model, determine the extent to which the order in which nodes arrived can be inferred from a single snapshot of the graph structure. I will describe both statistical limits and efficient algorithms for achieving those limits, and demonstrate the methods on a range of applications, from inferring the evolution of the human brain connectome to conventional citation and social networks. Finally, I briefly describe some ongoing projects that continue these lines of work.
Bio: Jithin K. Sreedharan is an NSF postdoctoral research associate at Center for Science of Information and at Dept. of Computer Science in Purdue University. He received his PhD from INRIA in France in 2017 with a fellowship from INRIA-Bell Labs joint lab. Before that, he finished MSc(Engg.) from Indian Institute of Science (IISc), Bangalore, in 2013, and received best MSc(Engg) thesis award from the division of EECS in IISc. His central research interest is in solving real-world problems in data science with a network perspective. His current works focus on data mining algorithms for large networks with probabilistic guarantees, statistical modeling and inference on networks, and distributed techniques for analyzing big network matrices. |
