Classification of Networks using Machine Learning Lab Logo Classification of Networks using Machine Learning Image of Research Project SEAP/NREIP Student: Kaahan Radia Dougherty Valley HS Class of 2017 kaahanradia2017@gmail.com Mentor: Dr. Ralucca Gera Applied Mathematics, NPS rgera@nps.edu Project Dates: June 20th – August 12th, 2016 Results / Accomplishments / Next Steps: Results: Demonstrated high accuracy in classifying social networks vs non social networks. Around 90% of the time, the machine learning algorithm was able to correctly classify given networks. Deliverable: A website that researchers can use to test their networks using our algorithm (used Django). Future Direction: it is vital to improve the database used in the machine learning algorithms, thus improving the classification accuracy of the algorithms. Project Objective and Research Approach: Aim: To classify networks into different predefined categories; technological, social, informational, and biological. Data: www.NetworkRepository.com Methodology: Used machine learning for the classification of the aforementioned data. Trained three machine learning algorithms (Random Forest, Support Vector, and Bayes) on the data from Network Repository using two python libraries, NetworkX and Scikit-Learn (metrics like clustering and cliques average used for training).