1. 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
Mentor:
Dr. Ralucca Gera
Applied Mathematics, NPS
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:
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).