Akash Narang

Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Katerina Potika

Second Advisor

Fabio Di Troia

Third Advisor

William Andreopoulos


Optimized Community Detection, Distributed Heterogeneous Servers


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.

Available for download on Sunday, May 25, 2025