2.5D Graph Navigation and Interaction Techniques

Summary

This project aim to design, implement and evaluate new methods and algorithms for effective and efficient navigation and interaction with 2.5D graph layout.

Supervisor(s)

Professor Seok-Hee Hong

Research Location

Information Technologies

Program Type

Masters/PHD

Synopsis

Recent technological advances have led to many large and complex network models in many domains, including social networks, biological networks, webgraphs and software engineering. Visualization can be an effective analysis tool for such networks; good visualisation may reveal the hidden structure of the networks and amplifies human understanding, thus leading to new insights, new findings and possible prediction of the future.

However, visualisation itself cannot serve as an effective and efficient analysis tool for large and complex networks, if it is not equipped with suitable interaction and navigation methods. Well designed and easy-to-use navigation and interaction techniques can enable the users to communicate with visualization much faster and effectively to perform various analysis tasks such as finding patterns, trends and unexpected events. 

Recently, 2.5D graph visualization methods have been successfully applied for visualization of large and complex networks, arising from biological networks, social networks and internet networks. However, the corresponding navigation method has yet been investigated so far. This project aim to design, implement and evaluate new methods for navigating 2.5D layouts of large and complex networks to enable users to perform analytical tasks.

Want to find out more?

Contact us to find out what’s involved in applying for a PhD. Domestic students and International students

Contact Research Expert to find out more about participating in this opportunity.

Browse for other opportunities within the Information Technologies .

Keywords

algorithm, graph drawing, Information visualization, interaction, social networks, biological networks

Opportunity ID

The opportunity ID for this research opportunity is: 1035

Other opportunities with Professor Seok-Hee Hong