21AI731 Social Network Analysis
Course Learning Objectives
CLO 1. Understand Semantic Web for social network analysis.
CLO 2. Learn the Representation, Modelling and Aggregating social network data.
CLO 3. Learn the basic algorithms and techniques for detection and decentralization of social network.
CLO 4. Study Human behaviour in social networks and its management.
CLO 5. Visual representation of social network data in different applications.
SYLLABUS COPY
MODULE - 1
Introduction to Semantic Web: Limitations of current Web – Development of Semantic Web – Emergence of the Social Web. Social Network analysis: Development of Social Network Analysis – Key concepts and measures in network analysis. Electronic sources for network analysis: Electronic discussion networks, Blogs and online communities – Web-based networks.
MODULE - 2
Knowledge Representation on the Semantic Web: Ontology and their role in the Semantic Web – Ontology based knowledge Representation – Ontology languages for the Semantic Web – Resource Description Framework and schema – Web Ontology Language. Modelling and aggregating social network data: State-of-the-art in network data representation – Ontological representation of social individuals – Ontological representation of social relationships – Aggregating and reasoning with social network data.
MODULE - 3
Detecting communities in social networks – Definition of community – Evaluating communities – Methods for community detection – Tools for detecting communities Decentralized online social networks – Introduction – Challenges for DOSN – The Case for Decentralizing OSNs – General Purpose DOSNs – Specialized Application Centric DOSNs – Social Distributed Systems – DelayTolerant DOSN.
MODULE - 4
Understanding and predicting human behaviour for social communities: User data management – Inference and Distribution – Enabling new human experiences – The Technologies. Managing Trust in Online Social Networks: Trust in online environment – Trust models based on subjective logic – Trust network analysis – Trust transitivity analysis – Combining trust and reputation – Trust derivation based on trust comparisons.
MODULE - 5
Visualization of Social Networks: Social Network Analysis – Visualization – Visualizing online social networks, Novel Visualizations and Interactions for Social Networks Exploration: Visualizing social networks with matrix-based representations – Matrix and Node-Link Diagrams – Hybrid representations. Applications of Social Network Analysis: Applications of Social Network Analysis – Covert networks – Community welfare – Collaboration networks – Co-Citation networks.
Course outcome
At the end of the course the student will be able to:
CO 1. Understand the Semantic Web and Electronic sources for social network analysis.
CO 2. Understand the Representation, Modelling and Aggregating social network data.
CO 3. Analyse the human behaviour in social network.
CO 4. Apply techniques for detection and decentralization of social network.
CO 5. Illustrate the visual representation of social network data.
Suggested Learning Resources
Text Books 1. Peter Mika, “Social Networks and the Semantic Web”, First Edition, Springer 2007.
2. Borko Furht, “Handbook of Social Network Technologies and Applications”, 1st Edition, Springer,2010. Reference
1. Guandong Xu ,Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and
applications”, First Edition Springer, 2011.
2. Dion Goh and Schubert Foo, “Social information Retrieval Systems: Emerging Technologies and
Applications for Searching the Web Effectively”, IGI Global Snippet, 2008.
3. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and Social Information
Retrieval and Access: Techniques for Improved user Modelling”, IGI Global Snippet, 2009.
4. John G. Breslin, Alexander Passant and Stefan Decker, “The Social Semantic Web”, Springer, 2009