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.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Kim, Daniel Jaewoo
• Pugmire, John Elias

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042419-220510

Advisor

• Keane, Patrick Gerard
• Walls, Robert Joseph

Year

• 2019

Date created

• 2019-04-24

Resource type

• Major Qualifying Project

Major

• Interdisciplinary

Rights statement

• In Copyright