Classification and Characterization of Networks

Faculty Sponsor

Karl R. B. Schmitt


Arts and Sciences


Mathematics and Statistics

ORCID Identifier(s)

0000-0002-5127-375X, 0000-0002-3887-8113, 0000-0003-3342-2835

Document Type

Poster Presentation

Symposium Date

Summer 7-31-2017


Networks are often labeled according to the underlying phenomena that they represent, such as re-tweets, protein interactions, or web page links. It is generally believed that networks from different categories have inherently unique network characteristics. Our research provides conclusive evidence to validate this belief by presenting the results of global network clustering and classification into common categories using machine learning algorithms. The machine learning techniques of decisions trees, random forests, linear support vector classification and Gaussian Naive Bayes were applied to a 14-feature 'identifying vector' for each graph. During cross-validation, the best technique, random forest, achieved an accuracy of 92%, a precision of 90% and a recall of 90%. After training the machine learning algorithm it was applied to a collection of initially unlabeled graphs from the Network Repository (www.networkrepository.com). Results were then manually checked by determining (when possible) original sources for these graphs. We conclude by examining the accuracy of our results and discussing how future researchers can make use of this process.

Biographical Information about Author(s)

James Canning is a rising sophomore at SUNY Geneseo, majoring in Mathematics and Physics. He has interests in mathematical programming, and was intrigued by this project because of its focus on computer science implementation of mathematical concepts. James hopes to continue his studies at a graduate school level.

Emma Ingram is a rising sophomore at the University of Alabama, majoring in Computer Science and Mathematics. She originally became interested in this project because she took a graph theory course in the Spring 2017 semester and wanted to learn more. She hopes to pursue either math or computer science at the graduate level.

Adriana M. Ortiz is a rising senior in the University of Puerto Rico, Rio Piedras Campus, pursuing a bachelor's degree in Mathematics. Adriana's interests are directed towards Discrete Mathematics courses, specifically in Graph Theory. After finishing her undergraduate degree, Adriana will continue her graduate studies in Applied Mathematics.

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