In this project, we are studying parallel and distributed 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, etc.) in the presence of dynamic changes to the graph in a distributed setup (for very large graphs).

TBA

• Ahmet Erdem Sarıyüce, Buğra Gedik, Gabriela Jacques-Silva, Kun-Lung Wu, Ümit V. Çatalyürek, Streaming Algorithms for k-core Decomposition, Very Large Data Bases Conference (VLDB), 2013.

• Erdem Sarıyüce, Ohio State University
• Gabriela Jacques da Silva, IBM T. J. Watson Research Center
• Kun-Lung Wu, IBM T. J. Watson Research Center
• Qiong Zou, IBM China Research Lab
• Rajesh Bordawekar, IBM T. J. Watson Research Center