TDSI Seminar : Network Science: Embracing Complexity in the Age of Connectivity

Course Description 

This course introduces the emerging discipline of network science, including the study of complex networks and their applications. We immerse students in a variety of networks, demonstrating and practicing techniques and models enabling insight into situations whose essence is best understood by networks, leading to greater awareness and even prediction. The topics discussed build from graph-theoretic concepts, and will address the mathematics of networks, network applications, and research applicability. We will expose students to the building blocks of network analysis, learn about the ongoing research in the field through presentations and research articles, and apply gained knowledge to the analysis of real and synthetic networks.

Complex networks – upon which we will focus throughout this course – are networks that feature patterns of connection between their elements that are  neither purely regular nor purely random. Such networks have gained increasing attention in recent years from those seeking to understand emerging phenomena in technology and society. Some examples of complex networks are on-line social networks, the Internet, the World Wide Web, neural networks, food-webs, metabolic networks, power grids, airline networks, national highway networks, the brain, and many more. We will study models created for these networks, beginning with random networks and followed by more sophisticated models of network formation: scale-free networks, small-world networks, and preferential attachment. We conclude with selective topics from the study of their properties such as networks’ degree distributions (Poisson vs. power law), centralities, shortest paths, clustering, robustness (resilience versus random attacks), and community detection, followed by ideas in new areas of current research.

Learning Objectives

Our goals for you, the participant, in this class are twofold. First, you will develop the mathematical sophistication needed to understand properties of complex networks. Second, armed with this understanding you will be able to identify and apply appropriate methodologies and techniques to answer questions using   network thinking and analysis. In doing this, you will

1. Analyze new networks using the main concepts of complex network analysis:

Þ identify network models and explain their structures;

Þ choose between several methodologies in analyzing networks;

Þ be able to grasp the meaning of a new research paper in complex networks;

2. Evaluate networks:

Þ contrast network models to explain emergent features of complex networks;    

Þ synthesize the new research work in this evolving area;  

Þ critique peer’s research;

3. Create new network research:

Þ design new network models building on the existing ones and available data;    

Þ design experiments to test hypothesis based on data to be analyzed;  

Þ generate new theory by expanding on the designed experiments.

Who Should Attend

A masters or undergraduate degree in a quantitative science is desired; or at minimum a course in discrete mathematics. Python knowledge is useful for a deeper analysis of networks; otherwise students will be working with a GUI application.

Learning Outcomes

At the end of the course, participants should be able to:

Þ Understand how to use network science concepts,

Þ Perform network science modeling based on real data, and

Þ Select correct methodologies needed to identify how to use knowledge on these complex networks to produce a research article or apply it in a real world situation. 

The learning outcomes above are achieved through building and analyzing network profile summaries and the critical reasoning obtained from the exposure to the other students’ network profile.  This gives participants the confidence to use gained network science experience as situations arise.

Enquiry and Registration
Please contact Ms Queenie Sim at tdsskeq@nus.edu.sg or +65 6516 5838 for more information.

Please refer here for more information.