{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To understand the components of the social network.
- To execute the models and visualize the social network using tools.
- To learn the mining of communities in the social network.
- To study the evolution of the social network.
- To know the applications in real time systems.
{{{unit}}}
| Unit I | Introduction | 9 |
The Semantic Web: Limitations of current web – Development of semantic web – Emergence of the social web; Statistical properties of social networks; Social Network Analysis: Development of social network analysis – Key concepts and measures in network analysis – Discussion; Blogs and online communities – Web-based networks.
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| Unit II | Modeling and Visualization | 9 |
Modelling and aggregating social network data: Aggregating and reasoning with social network data – Advanced representations; Visualization of Social Networks: Centrality – Clustering – Node-Edge diagrams; Visualizing online social networks: Node-Link Diagrams – Matrix-based representations
{{{unit}}}
| Unit III | Mining Communities | 9 |
Random walks in social networks and their applications: – RandomWalks on graphs – Algorithms – Applications; Detecting Communities in Social Networks: Core methods – Emerging fields and problems; Node classification in social networks.
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| Unit IV | Evolution | 9 |
Evolution in Social Networks: Framework – Tracing smoothly evolving communities; Models and Algorithms for Social Influence Analysis: Influence related statistics – Social similarity and influence – Influence maximization in viral marketing; Algorithms and Systems for Expert Location in Social Networks: Expert location without graph constraints – Score propagation – Expert team formation; Link Prediction in Social Networks: Feature based link prediction – Bayesian probabilistic models – Probabilistic relational models
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| Unit V | Applications | 9 |
A learning based approach for real time emotion classification of tweets; A new linguistic approach to assess the opinion of users in social network environments; Explaining scientific and technical emergence forecasting; Social network analysis for biometric template protection
\hfill Total:45
After the completion of this course, students will be able to:
- Understand the components of the social network(K2).
- Have working knowledge on the models and visualize the social network using tools(K3).
- Gain knowledge on mining of communities in the social network(K2).
- Understand the evolution of the social network(K2).
- Work on the applications in real time systems(K3).
- Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
- Borko Furht,“Handbook of Social Network Technologies and Applications”, Springer, 1st edition, 2011
- Charu C. Aggarwal, “Social Network Data Analytics”, Springer, 2014
- Przemyslaw Kazienko, Nitesh Chawla,“Applications of Social Media and Social Network Analysis”, Springer,2015
- Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”, Springer, 2010.
- Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and applications”, Springer, 1st edition, 2012
- Ajith Abraham, Aboul Ella Hassanien, Vaclav Snasel, “Computational Social Network Analysis: Trends, Tools and Research Advances”, Springer, 2012