Course

Unit 1

Introduction: Applications, Preliminaries, Three Levels of SNA, Graph Visualization Tools.Network Measures: Network Basics, Node Centrality, Assortativity, Transitive and Reciprocity, Similarity.

Unit 2

Network Growth Models: Properties of real-world networks, Random network model. Preferential Attachment model, Prices model Local-world network growth model.

Unit 3

Link Analysis: Applications, Strong and weak ties Link analysis and algorithms, Page Rank. Community Structure in Networks: Applications, Types of Community detection methods, Disjoint community detection, overlapping community detection, local community detection.

Unit 4

Cascade Behavior and Network Effects: Cascade model probabilistic cascade Cascade prediction. Anomaly Detection in Static Networks: Outlier vs Network-based anomalies, challenges in anomaly detection.

Unit 5

Graph Representation Learning: Machine Learning pipelines, Intuition behind representational learning benefits representational learning methods.Case Study: Analysis of social network datasets, Modeling the spread of COVID 19, Recommendation System.

Text Books / References

Text Books:

1. Tanmoy Chakraborty (2021) Social Network Analysis, Wiley.

References:

1. Albert-Lazzlo Barabasi (2016). Network Science, Cambridge University
2. Stanley Wasserman and Katherine Faust (1994). Social Network Analysis: Methods and Applications. Cambridge university press.

This course aims to equip students with the skills to model, analyse, and visualize complex networks (social, technological, or information) to uncover structural properties, identify influential nodes, detect communities, and understand information propagation or behavioural dynamics using data-driven methods.

Course Outcomes: After successful completion of the course, students will be able to

• CO1: Understand the levels of SNA and network measures
• CO2: Familiarize with network growth models
• CO3: Gain knowledge about different community structures and link prediction models
• CO4: Understand cascade behavior in networks
• CO5: Predict and recommend in online social networks

CO-PO Mapping: