Student research opportunities
Research network predictive analytics
Project Code: CECS_766
This project is available at the following levels:
Honours, Masters, PhD
Keywords:
social network analysis, link data mining, bibliographic data, research data, temporal data analysis
Supervisors:
Dr Kee Siong NgDr Paul Wong
Assoc Professor Peter Christen
Outline:
Researchers across the globe often collaborate by co-authoring papers together. They publish their research findings in conferences and journals that cite other relevant papers from their own or neighbouring fields of research. As such, researchers form a global social network ("who co-authored with whom") and their published papers form a global document network ("which papers are cited by which other papers").
Goals of this project
The aim of this project is to draw on a variety of scientific techniques (e.g. network science, data mining, machine learning) and technologies (e.g. parallel database, XML, data matching) to analyse the evolution of the global research network over the last 15 years in order to make novel predictions about its future. In particular, we are interested in identifying significant “emerging areas of research” on par with the discovery and advent of string theory, network science, data mining etc.
Requirements/Prerequisites
Projects in this research area are available both as one-year Computer Science Honours, or as multi-year MPhil or PhD projects.
Students interested undertaking this project should have good programming skills, and knowledge in areas such as algorithms and data structures, graph theory, network science, parallel computing, data mining, and machine learning.
Students interested undertaking this project as a MPhil or PhD student should hold the equivalent of an Australian Bachelors degree with Honours 2A level or above in computer science, and preferably have done their honours research in the areas of network analysis, data mining, or machine learning.
Student Gain
This is an exciting and challenging project that will involve the analysis of real world data, cutting edge technologies, advanced scientific techniques, and cross sectorial collaboration between academia (Research School of Computer Science), university administration (Central Research Office) and industry (EMC Greenplum).
Background Literature
- Networks: An Introduction, by Mark Newman, Oxford University Press, 2010.
- Networks, Crowds and Markets: Reasoning about a Highly Connected World, by David Easley and Jon Kleinberg, Cambridge University Press, 2010 (online version http://www.cs.cornell.edu/home/kleinber/networks-book/)
- A very interesting documentary " Connected: The Power of Six Degrees " produced in Australia about the history of Network Science: http://ivl.slis.indiana.edu/km/movies/2008-talas-connected.mov
