Course

PROFESSIONAL ELECTIVES

Electives Electives in Data Science

Unit I

Networks and Society: Introduction of SNA; Three Levels of Social Network Analysis; Graph Visualisation Tools;Network Measures: Network Basics, Node Centrality, Assortativity, Transitivity and Reciprocity, Similarity, Degeneracy. Network Growth Models: Properties of Real-World Networks; Random Network Model; Ring Lattice Network Model; Watts–Strogatz Model; Preferential Attachment Model; Price’s Model; Local-world Network Growth Model; Network Model with Accelerating Growth; Aging in Preferential Attachment – Link Analysis: Applications of Link Analysis; Signed Networks; Strong and Weak Ties; Link Analysis Algorithms; PageRank; Personalised PageRank; DivRank; SimRank; PathSIM

Unit II

Community Structure in Networks: Applications of Community Detection, Types of Communities, Community Detection Methods; Disjoint Community Detection; Overlapping Community Detection; Local Community Detection; Community Detection vs Community Search; Evaluation of Community Detection Methods – Link Prediction: Applications of Link Prediction, Temporal Changes in a Network; Problem Definition; Evaluating Link Prediction Methods; Heuristic Models; Probabilistic Models; Supervised Random Walk; Information-theoretic Model; Latest Trends in Link Prediction – Cascade Behaviours and Network Effects: Preliminaries and Important Terminologies; Cascade Models; Case Study – The “Indignados” Movement; Probabilistic Cascades; Epidemic Models; Independent Cascade Models; Cascade Prediction

Unit III

Anomaly Detection in Networks: Outliers versus Network-based Anomalies; Challenges; Anomaly Detection in Static Networks; Anomaly Detection in Dynamic Networks – Graph Representation Learning: Machine Learning Pipelines; Intuition behind Representation Learning; Benefits of Representation Learning; Criterion for Graph Representation Learning; Graph Representation Learning Pipeline; Representation Learning Methods – Applications and Case Studies: Malicious Activities on OSNs; Sockpuppets in OSNs; Collusion on Online Social Networks; Modelling the Spread of a pandemic

 Course Objective

• To introduce the basic concept and structure of social networks and its implications.
• To focus on analysing massive networks, which provide many computational, algorithmic, and modelling challenges.
• To enable students to practically analyze large-scale network data and how to reason about it through models for network structure and evolution.

Course Outcomes

CO1: Understand the concept and structure of social networks and its implications.

CO2: Understand the measures and metrics used in social networks and its computation.

CO3: Explore social media data and analyze it.

CO4: Perform analysis of social network data using machine learning techniques.

CO5: Community identification and link prediction in social networks based on graph processing techniques

in Python.

CO-PO Mapping

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports

*CAL – Can be Lab Assessments, Project, and Report

**End Semester can be theory examination/ lab-based examination/ project presentation

Textbook(s)

Tanmoy Chakraborty,” Social Network Analysis”, Wiley, 2021.

Reference(s)

Matthew A. Russell, Mikhail Klassen, “Mining the Social Web”, 3rd Edition, O’Reilly, 2019.

Albert-Lazzlo Barabasi, “Network Science”, Cambridge University Press, 2016.

Stanley Wasserman, Katherine Faust, “Social Network Analysis: Methods and Applications”, Cambridge University Press, 2012.