Evaluating the Readability of Graph Layouts: A Deep Learning Approach

MPhil Thesis Defence


Title: "Evaluating the Readability of Graph Layouts: A Deep Learning Approach"

By

Mr. Hammad HALEEM


Abstract

Existing graph layout algorithms are usually not able to optimize all the 
aesthetic properties desired in a graph layout. To evaluate how well the 
desired visual features are exhibited in a graph layout, many readability 
metrics were introduced in the past decade. However, the calculation of these 
readability metrics often requires access to the node and edge coordinates and 
is usually computationally inefficient, especially for dense graphs. 
Importantly, when the node and edge coordinates are not accessible, it becomes 
impossible to evaluate the graph layouts quantitatively. We propose a novel 
deep-learning based approach to assess the readability of layouts by directly 
using graph images. A convolutional neural network architecture is proposed, 
trained on a benchmark dataset of graph images, which is composed of 
synthetically-generated and graphs created by sampling from real large 
networks. The proposed method is quantitatively compared to traditional methods 
and qualitatively assessed with the help of a case study. This work is a first 
step towards using deep learning based approach to evaluate images from the 
visualization field quantitatively.


Date:			Wednesday, 4 July 2018

Time:			4:00pm - 6:00pm

Venue:			Room 5501
 			Lifts 25/26

Committee Members:	Prof. Huamin Qu (Supervisor)
 			Dr. Pedro Sander (Chairperson)
 			Dr. Xiaojuan Ma


**** ALL are Welcome ****