Date of Award

Spring 6-4-2025

Document Type

Thesis (Undergraduate)

Department

Computer Science

First Advisor

Deeparnab Chakrabarty

Abstract

In this thesis we consider the problem of harmonic centrality in graphs. This measure is used widely in the study of real-world complex networks. In particular, we consider the problem of finding a high harmonic centrality vertex in time much faster than $O(mn)$, the time required to calculate the exact harmonic centrality of all vertices in a graph. The problem of calculating centrality measures faster has received much attention in recent years, since calculating the exact harmonic centrality of all vertices can be infeasible for large graphs; hence, faster algorithms are needed. This thesis proposes a new algorithm for finding a high harmonic centrality vertex in a graph quickly. The algorithm leverages depth-limited BFS from random vertices within the graph to return a vertex whose harmonic centrality is comparable to the highest harmonic centrality in the graph. We present our findings theoretically, as well as experimentally, on large collaboration, social, and image metadata graphs.

Download

Share

COinS