| CS6010 | SOCIAL NETWORK ANALYSIS | L | T | P C |
| 3 | 0 | 0 3 |
The student should be made to:
- Understand the concept of semantic web and related applications.
- Learn knowledge representation using ontology.
- Understand human behaviour in social web and related communities
- 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.
OUTCOMES:
Upon completion of the course, the student 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.
TOTAL: 45 PERIODS
TEXT BOOKS:
- Peter Mika, “Social Networks and the Semantic Web”, , First Edition, Springer 2007.
- Borko Furht, “Handbook of Social Network Technologies and Applications”, 1st Edition, Springer,
REFERENCES:
- Guandong Xu ,Yanchun Zhang and Lin Li, “Web Mining and Social Networking – Techniques and
- Dion Goh and Schubert Foo, “Social information Retrieval Systems: Emerging Technologies and
- Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and Social Information
- John G. Breslin, Alexandre Passant and Stefan Decker, “The Social Semantic Web”, Springer,
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