Research

Traditional graph theory and most current research in graph modeling, querying, and mining concentrates on problems where the graph structure is inherently static and does not change with time. But networks in the real world are dynamic in nature with a wide range of temporal changes — while the topology of networks such as social networks and transportation networks undergoes gradual change (or evolution), the content (information flow, annotations) changes more rapidly. A dynamic graph that comprises of a series of static graph snapshots has fundamentally different properties than the union of properties of constituent static graph snapshots. My research group is working on models for dynamic graphs, how to extrapolate properties of dynamic graphs on the basis of a sample, how to relate the content of a message with its flow in a network, models for users, and detection of significant substructures.

In research specific to biological networks, my group is developing models by integrating SNPs, gene expression, and phenotypic data to explain how natural genetic variation leads to phenotypic changes. We are also exploring brain networks as dynamic networks where the level of activity in a region or between regions changes during learning or disease. The goal here is to discover network biomarkers that can explain different outcomes.