NC4001 Network Analytics Syllabus:
NC4001 Network Analytics Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
After the completion of the course the student will be able
To provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data.
To introduce basic network models and structural descriptors, network dynamics
To mine the users in the social network.
To understand the evolution of the social network.
To know the applications in real time systems.
UNIT I INTRODUCTION
Overview: Social network data-Formal methods- Paths and Connectivity-Graphs to represent social relations-Working with network data- Network Datasets-Strong and weak ties – Closure, Structural Holes, and Social Capital.
UNIT II SOCIAL INFLUENCE
Homophily: Mechanisms Underlying Homophily, Selection and Social Influence, Affiliation, Tracking Link Formation in OnLine Data, Spatial Model of Segregation – Positive and Negative Relationships – Structural Balance – Applications of Structural Balance, Weaker Form of Structural Balance.
UNIT III INFORMATION NETWORKS AND THE WORLD WIDE WEB
The Structure of the Web- World Wide Web- Information Networks, Hypertext, and Associative Memory- Web as a Directed Graph, Bow-Tie Structure of the Web- Link Analysis and Web Search Searching the Web: Ranking, Link Analysis using Hubs and Authorities- Page Rank- Link Analysis in Modern Web Search, Applications, Spectral Analysis, Random Walks, and Web Search.
UNIT IV SOCIAL NETWORK MINING
Clustering of Social Network graphs: Betweenness, Girvan newman algorithm-Discovery of communities- Cliques and Bipartite graphs-Graph partitioning methods-Matrices-Eigen values Simrank.
UNIT V NETWORK DYNAMICS
Cascading Behavior in Networks: Diffusion in Networks, Modeling Diffusion – Cascades and Cluster, Thresholds, Extensions of the Basic Cascade Model- Six Degrees of Separation-Structure and Randomness, Decentralized Search- Empirical Analysis and Generalized Models- Analysis of Decentralized Search.
COURSE OUTCOMES:
At the end of the course student will be able to
CO1: understand the underpinnings of search engines and webpage ranking
CO2: make sense of large graphs, ranging from social networks to the smart power grid
CO3: have a good understanding of prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis
CO4: Analyze the network flow data
CO5: Estimate the size of the Internet
TOTAL:45 PERIODS
REFERENCES
1. Easley and Kleinberg, “Networks, Crowds, and Markets: Reasoning about a highly connected world”, Cambridge Univ. Press, 2010.
2. Robert A. Hanneman and Mark Riddle, “Introduction to social network methods”, University of California, 2005.
3. Jure Leskovec,Stanford Univ.Anand Rajaraman,Milliway Labs, Jeffrey D. Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2 edition, 2014.
4. Wasserman, S., & Faust, K, “Social Network Analysis: Methods and Applications”, Cambridge University Press, 2009.
5. Borgatti, S. P., Everett, M. G., & Johnson, J. C., “Analyzing social networks”, SAGE Publications Ltd; 1 edition, 2013.
6. John Scott , “Social Network Analysis: A Handbook” , SAGE Publications, 2nd edition, 2000.