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Community
structure
Real-world
networks often exhibit community structure in them. Thus, finding these
communities is crucial to understand the dynamics and function of these
networks. I continue to work on devising methods and algorithms to capture
community structure in networks. Some of these methods are also used to analyze
Omics data.
Biological
networks
Identifying functional
units in biological networks (e.g., gene co-expression, protein-protein
interaction networks) is an important problem in systems biology. Despite
abundance of data, determining how genes or proteins interact and operate
together to control the function at the system level is still not well
understood. I am particularly interested in applying theoretical and
computational methods to understand the topological structure and function of
these molecular interaction networks.
Social
dynamics
Simple
models can be useful to gain insights into evolution/spreading of opinions in
social systems. One of my research interests has been to study models of social
influence in networks, where the interaction among individuals is governed by
widely accepted social theories (diffusion, homophily, structural balance
etc.).