MSc Thesis Defense Announcement by Susha Suresh:"Attributed Graph Classification via Deep Graph Convolutional Neural Networks"

Wednesday, November 20, 2019 - 13:30 to 15:30



The School of Computer Science is pleased to present…

MSc Thesis Defense by: Susha Suresh
Date:  Wednesday November 20th, 2019
Time:  1: 30 pm – 3:30 pm
Location: 3105, Lambton Tower


From social networks to biological networks, graphs are a natural way to represent a diverse set of real-world data. This research presents attributed graph convolutional neural network with a pooling layer (AGCP for short), a novel end-to-end deep neural network model which captures the higher-order latent attributes of weighted, labeled, undirected, attributed graphs of arbitrary size. The architecture of AGCP is an efficient variant of convolutional neural network (CNN) and has a linear filter function that convolves over the fixed topological structure of a graph to learn local and global attributes of the graph. Convolution is followed by a pooling layer that coarsens the graph while preserving the global structure of the original input graph using information gain. On the other hand, advances in high throughput technologies for next-generation sequencing have enabled machine learning research to acquire and extract knowledge from biological networks. We apply AGCP on three bioinformatics networks, ENZYMES, D&D, and GINA a graph dataset of gene interaction networks with genomic mutation attributes as the attributes of the vertices. In several experiments on these datasets, we demonstrate that AGCP yields better results in terms of classification accuracy relative to the previously proposed models by a considerable margin.

Thesis Committee:

Internal Reader: Dr. Jianguo Lu
External Reader: Dr. Esam Abdel-Raheem
Advisor: Dr. Dan Wu and Dr. Luis Rueda
Chair: TBD

MSc Thesis Defense Announcement

5113 Lambton Tower, 401 Sunset Ave., Windsor ON., N9B 3P4 (519)253-3000 Ext. 3716