MC4012 Social Network Analytics Syllabus:
MC4012 Social Network Analytics Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To gain knowledge about social networks, its structure and their data sources.
To study about the knowledge representation technologies for social network analysis.
To analyze the data left behind in social networks.
To gain knowledge about the community-maintained social media resources.
To learn about the visualization of social networks.
UNIT I INTRODUCTION TO SEMANTIC WEB
The development of Semantic Web – Emergence of the Social Web – The Development of Social Network Analysis – Basic Graph Theoretical Concepts of Social Network Analysis – Electronic Sources for Network Analysis – Electronic Discussion Networks, Blogs and Online Communities
UNIT II KNOWLEDGE REPRESENTATION ON THE SEMANTIC WEB
Ontology-based knowledge Representation – Ontology languages for the Semantic Web: RDF and OWL
UNIT III SOCIAL NETWORK MINING
Detecting Communities in Social Network – Evaluating Communities –Methods for Community Detection – Applications of Community Mining Algorithms – Tools for detecting communities – Application: Mining Facebook
UNIT IV COMMUNITY MAINTAINED SOCIAL MEDIA RESOURCES
Community Maintained Resources – Supporting technologies for community maintained resources– User motivations-Location based social interaction – location technology– mobile location sharing – Automated recommender system
UNIT V VISUALIZATION OF SOCIAL NETWORKS
Visualization of Social Networks – Node-Edge Diagrams – Random Layout – Force-Directed Layout – Tree Layout – Matrix Representations –Matrix and Node-Link Diagrams– Visualizing Online Social Networks
TOTAL: 45 PERIODS
SUGGESTED ACTIVITIES:
1. Create complex topologies for a social network (Eg: Society of Friends (Quakers) https://programminghistorian.org/assets/exploring-and-analyzing-network-data-withpython/quakers_nodelist.csv) using an open source library (NetworkX) and analyse multiple metrics (Node degree, Node strength, Average path length, Clustering coefficient, Node centralities and Ego-betweenness centrality).
2. Describe the steps in Ontology development using Uniform Modeling Language. Also discuss how to interact with the ontology by extending UML.
3. Collect different types of data from Twitter by using an open source library (Tweepy) and build your own Twitter data crawler.
4. Discuss about community welfare application in social network analysis using an open source tool ( Gephi).
5.Consider a data set (eg: Flavor Network https://github.com/lingcheng99/FlavorNetwork/tree/master/data).Transform mathematical representations of the given network (adjacency matrix) with features (eg: flavour compounds) into a graphical representation (Node-Edge Diagrams).
COURSE OUTCOMES:
Up on completion of the course, the students will be able to:
CO1:create entities and relationships of data as network and do analysis
CO2:Model and represent knowledge for social semantic Web.
CO3:Use extraction and mining tools for analyzing Social networks.
CO4:Collect data from various social media resources and analyse.
CO5:Develop personalized visualization for Social networks.
REFERENCES
1. Matthew A. Russell,“Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, Githuband more”, O’REILLY, Third Edition, 2018.
2. CharuAggarwal, “Social Network Data Analytics,” Springer, First Edition, 2014
3. Jennifer Golbeck, “Analyzing the social web”, Waltham, MA: Morgan Kaufmann (Elsevier), First Edition, 2013.
4. BorkoFurht, “Handbook of Social Network Technologies and Applications”, Springer, First Edition, 2010
5. Peter Mika, “Social Networks and the Semantic Web”, Springer, First Edition, 2007