Date of Award

Spring 5-2020

Degree Type


Degree Name


Degree Program

Computer Science


Computer Science

Major Professor

Dr. Shaikh Arifuzzaman

Second Advisor

Dr. Minhaz Zibran

Third Advisor

Dr. Vassil Roussev

Fourth Advisor

Dr. Mahdi Abdelguerfi


There are several approaches for discovering communities in a network (graph). Despite being approximating in nature, discovering communities based on the laws of Information Theory has a proven standard of accuracy. The information-theoretic algorithm known as Infomap developed a decade ago for detecting communities, did not foresee the tremendous growth of social networking, multimedia, and massive information boom. To discover communities in massive networks, we have designed a distributed-memory-parallel Infomap in the MPI framework. Our design reaches scalability of over 500 processes capable of processing networks with millions of edges while maintaining quality comparable to the sequential Infomap. We have further developed a novel parallel hybrid approach for Infomap consists of both distributed and shared memory parallelism using MPI and OpenMP frameworks. This achieves a speedup of more than 11x in processing a network of over 100 million edges which is significantly greater than the state-of-the-art techniques.


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