In this project, we are studying parallel, distributed, and stream processing techniques for managing large-scale graphs. Such graphs are becoming increasingly common in application domains like social network analytics, web graph analysis, bioinformatics, etc. These graphs can be highly dynamic, very large, or both dynamic and large. We investigate system-level as well as analytic techniques for addressing resulting challenges.
In this project, we look at system level techniques to improve performance of BSP style (Pregel-like) graph processing infrastructures on shared memory processors (SMPs).
In this project, we look at incremental maintenance of important graph structures (such as k-core, k-truss, communities, etc.) in the presence of dynamic changes to the graph in a distributed setup (for very large graphs).
Interaction graphs are append-only temporal graphs where interactions between entities are continuously added. In this project we look at streaming algorithms to best package edges to disk blocks, so that disk based interaction graph DBs can perform query processing more efficiently w.r.t. disk I/O.