Date of Award
Spring 5-2020
Degree Type
Thesis-Restricted
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Shaikh Arifuzzaman
Second Advisor
Dr. Minhaz Zibran
Third Advisor
Dr. Vassil Roussev
Fourth Advisor
Dr. Mahdi Abdelguerfi
Abstract
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.
Recommended Citation
Faysal, Md Abdul Motaleb, "Accelerating the Information-Theoretic Approach of Community Detection Using Distributed and Hybrid Memory Parallel Schemes" (2020). University of New Orleans Theses and Dissertations. 2739.
https://scholarworks.uno.edu/td/2739
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.