Faculty Advisor

Keane, Patrick Gerard

Faculty Advisor

Walls, Robert Joseph

Abstract

In their 2013 paper, Cao et. al introduce the diffusion state distance (DSD) metric on graphs for use in the vertex labeling problem on protein-protein interaction networks. We generalize their classification approach, which uses weighted k-nearest neighbors voting, to work with any graph metric. We analyze the performance of this approach on graphs resembling real-world networks using shortest-path distance, DSD, and resistance distance. To this end, we propose novel simulation models to generate labeled scale-free and small-world networks, and perform label prediction experiments on the simulated graphs as well as real-world networks. We conclude that the DSDbased prediction algorithm exhibits more robust community awareness than the ones using the other metrics.

Publisher

Worcester Polytechnic Institute

Date Accepted

April 2019

Major

Interdisciplinary

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Mathematical Sciences

Advisor Department

Computer Science

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