ANNA UNIVERSITY CSE SYLLABUS
CS6010 SOCIAL NETWORK ANALYSIS SYLLABUS
8TH SEM CSE SYLLABUS
8TH SEM CSE SYLLABUS
REGULATION 2013
CS6010 SOCIAL NETWORK ANALYSIS SYLLABUS |
OBJECTIVES:
-> 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
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
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
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
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
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
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.
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.
REFERENCES:
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.
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