Publication Date
Spring 2024
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Katerina Potika
Second Advisor
Fabio Di Troia
Third Advisor
William Andreopoulos
Keywords
Optimized Community Detection, Distributed Heterogeneous Servers
Abstract
The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, improving F1 score results. We also implemented ParallelCommunityProcessor algorithm inside bolt to utilize parallelism in processing community sets. Experiments on various datasets demonstrate that these approaches yield superior results.
Recommended Citation
Narang, Akash, "Optimized Community Detection across Distributed Heterogeneous Servers" (2024). Master's Projects. 1395.
DOI: https://doi.org/10.31979/etd.zwm7-yw6j
https://scholarworks.sjsu.edu/etd_projects/1395
