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Core/Elective: Core Semester: 2 Credits: 4

Course Description

Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Recent trends indicate the usage of social networks as a key feature for next generation usage and exploitation of the Web. This course provides an opportunity to learn social network analysis.

Course Objectives

Representation and analysis of social networks

Course Content

1. Networks of information – Mathematics of networks – Measures and metrics – Large scale strucuture of networks – Matrix algorithms and graph partitioning

2. Network models – Random graphs – walks on graphs - Communitiy discovery – Models of network formation – Small world model - Evolution in social networks

3. Processes on networks – Percolation and network resilience – Epidemics on networks – Dynamical systems on networks – Network search

4. Models for social influence analysis – Systems for expert location – Link prediction – privacy analysis – visulalization – Data and text mining in social networks - Social tagging

5. Social media - Analytics and predictive models – Information flow – Modeling and prediction of flow - Missing data - Social media datasets – patterns of information attention – linear influence model – Rich interactions


1.Networks: An introduction: Mark Newman, Oxford University Press (2010)
2.Social Network Data Analytics: Charu C Aggarwal (ed.), Springer (2011)
3.Networks, Crowds, and Markets: Reasoning about a highly connected World, David Easley and Jon Kleinberg, Cambridge University Press (2010)
4.Understanding Social Networks: Theories, Concepts, and Findings: Charles Kadushin, OUP (2012)

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