Date Approved

1-12-2026

Embargo Period

1-12-2026

Document Type

Thesis

Degree Name

M.S. Computer Science

Department

Computer Science

College

College of Science & Mathematics

Advisor

Guimu Guo, Ph.D.

Committee Member 1

Sihan Yu, Ph.D.

Committee Member 2

Richard Rabbitz, MSc.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Abstract

The Densest Subgraph Discovery (DSD) problem is a prevalent problem in the field of graph mining, aiming to find the cohesive subgraph. Given a graph �� = (��,��) and an integer �� ≥ 2, the goal is to find a vertex sub- set �� ⊆ �� whose induced subgraph �� (��) maximizes the ��-clique density, defined as the number of ��-cliques per vertex. Larger val- ues of �� capture higher-order connectivity patterns beyond edges, enabling the discovery of more cohesive structures. There have been many solutions to this problem. However, one avenue that other graph mining problems have gone down is parallel programming, which executes many commands simultaneously for more efficient programs. What we have done is applied parallel programming on the GPU to a DSD solution called CoreExact in a two-fold effort. This is done in hopes of both creating a more efficient solution to the DSD while also serving as further research and precedent for the efficacy of parallel programming using the GPU.

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