CS8085-SOCIAL NETWORK ANALYSIS Syllabus 2017 Regulation
SOCIAL NETWORK ANALYSIS Syllabus 2017 Regulation,CS8085-SOCIAL NETWORK ANALYSIS Syllabus 2017 Regulation
CS8085 SOCIAL NETWORK ANALYSIS L T P C 3 0 0 3
- To understand the concept of semantic web and related applications.
- To learn knowledge representation using ontology.
- To understand human behaviour in social web and related communities.
- To learn visualization of social networks.
UNIT I INTRODUCTION 9
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 – Applications of Social Network Analysis.
UNIT II MODELLING, AGGREGATING AND KNOWLEDGE REPRESENTATION 9
Ontology and their role in the Semantic Web: Ontology-based knowledge Representation – Ontology languages for the Semantic Web: Resource Description Framework – 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 – Advanced representations.
UNIT III EXTRACTION AND MINING COMMUNITIES IN WEB SOCIAL NETWORKS 9
Extracting evolution of Web Community from a Series of Web Archive – Detecting communities in social networks – Definition of community – Evaluating communities – Methods for community detection and mining – Applications of community mining algorithms – Tools for detecting communities social network infrastructures and communities – Decentralized online social networks – Multi-Relational characterization of dynamic social network communities.
UNIT IV PREDICTING HUMAN BEHAVIOUR AND PRIVACY ISSUES 9
Understanding and predicting human behaviour for social communities – User data management – Inference and Distribution – Enabling new human experiences – Reality mining – Context – Awareness – Privacy 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 – Attack spectrum and countermeasures.
UNIT V VISUALIZATION AND APPLICATIONS OF SOCIAL NETWORKS 9
Graph theory – Centrality – Clustering – Node-Edge Diagrams – Matrix representation – Visualizing online social networks, Visualizing social networks with matrix-based representations – Matrix and Node-Link Diagrams – Hybrid representations – Applications – Cover networks – Community welfare – Collaboration networks – Co-Citation networks.
TOTAL: 45 PERIODS
Upon completion of the course, the students should be able to:
- Develop semantic web related applications.
- Represent knowledge using ontology.
- Predict human behaviour in social web and related communities.
- Visualize social networks.
- Peter Mika, ―Social Networks and the Semantic Web, First Edition, Springer 2007.
- Borko Furht, ―Handbook of Social Network Technologies and Applications, 1st Edition, Springer, 2010.
- Guandong Xu ,Yanchun Zhang and Lin Li,-Web Mining and Social Networking – Techniques and applications, First Edition, Springer, 2011.
- Dion Goh and Schubert Foo,-Social information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively, IGI Global Snippet, 2008.
- Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, Collaborative and Social Information Retrieval and Access: Techniques for Improved user Modelling, IGI Global Snippet, 2009.
- John G. Breslin, Alexander Passant and Stefan Decker, -The Social Semantic Web, Springer, 2009.