Syllabus
Unit I
Introduction: Applications, Preliminaries, Three Levels of SNA, Graph Visualization Tools.
Network Measures: Network Basics, Node Centrality, Assortativity, Transitive and Reciprocity, Similarity.
Network Growth Models: Properties of real-world networks, Random network model. Preferential Attachment model, Price’s model – Local-world network growth model.
Unit II
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 III
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.
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/ Reference Books:
- Social Network Analysis, Tanmoy Chakraborty, Wiley, 2021.
- Network Science, Albert-Lazzlo Barabasi.
- Social Network Analysis: Methods and Applications, Stanley Wasserman, Katherine Faus.