CS6010 SOCIAL
NETWORK ANALYSIS L T P C 3
0 0 3
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 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.
UNITII 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
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, Alexandre Passant and Stefan Decker, “The
Social Semantic Web”, Springer, 2009.
No comments:
Post a Comment