Topic - Mining communities in big-data with algorithms and computational thinking
Big Data and Cloud Computing
About
This topic will teach students how to use Computational Thinking and algorithms for community detection in large graphs (big-data) to solve real world problems.
Computational thinking is about applying powerful ideas in computer science (problem formulation, abstraction, decomposition, pattern recognition, and algorithm design) to tackle real world problems.
We then combine this with modeling of big data using graphs and using community detection algorithms to identify communities in these graphs. Identification of communities in these big-data networks can help to provide give new insights to how the individual nodes interact and the role of certain nodes in the communities (the driver nodes, the hubs and authorities in the graph), can also help to condense large graphs into communities, and can help in visualization of these large graphs which is helpful in many other big-data analytics tasks.
Prerequisites
In general, students should already have some good experience with basic data structures: arrays, matrices, stacks, queues, trees, hashing, graphs, etc. And knows complexity analysis of algorithms using the big-O notation.
Specifically, to take this topic, students must have
Computational thinking is about applying powerful ideas in computer science (problem formulation, abstraction, decomposition, pattern recognition, and algorithm design) to tackle real world problems.
We then combine this with modeling of big data using graphs and using community detection algorithms to identify communities in these graphs. Identification of communities in these big-data networks can help to provide give new insights to how the individual nodes interact and the role of certain nodes in the communities (the driver nodes, the hubs and authorities in the graph), can also help to condense large graphs into communities, and can help in visualization of these large graphs which is helpful in many other big-data analytics tasks.
Prerequisites
In general, students should already have some good experience with basic data structures: arrays, matrices, stacks, queues, trees, hashing, graphs, etc. And knows complexity analysis of algorithms using the big-O notation.
Specifically, to take this topic, students must have
- taken two or more programming and data-structure courses,
- actually programmed with graph algorithms, and
- good software development skills.
Professor LEONG Hon Wai
Department of Computer Science, School of Computing, NUS
Department of Computer Science, School of Computing, NUS
Introduction
Prof Leong received the B.Sc. (Hon) in Mathematics from University of Malaya and Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign.
His research interests are in the design and analysis of algorithms, optimization and discrete mathematics. He works in many areas including VLSI design, transportation logistics, multimedia systems, and computational biology.
In Singapore, he started the Singapore training program for the IOI (International Olympiad in Informatics). He is a member of ACM, IEEE, ISCB, and a Fellow of the Singapore Computer Society.
His research interests are in the design and analysis of algorithms, optimization and discrete mathematics. He works in many areas including VLSI design, transportation logistics, multimedia systems, and computational biology.
In Singapore, he started the Singapore training program for the IOI (International Olympiad in Informatics). He is a member of ACM, IEEE, ISCB, and a Fellow of the Singapore Computer Society.
CONTACT
Want to know more ? You can contact us.
Email: sws@comp.nus.edu.sg
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