CSL7390: Social Network Analysis

Offered in Autumn 2021 Semester

Syllabus

• SNA Introduction
• Graph Recap: Graph Introduction: Adjacency Matrix, Paths, Connectivity, Incidence Matrix, Distance, Breadth-First-Search, Directed Graph
• Network Measures, Centrality, Strong and Weak Ties, Cliques, Component, Structural Balance
• Network Models: Random Networks, Scale-Free Networks, Barabási-Albert Model, Fuzzy-Granular Social Network, Generating Network Data
• Game Theory and Network Traffic Modeling
• Information Cascade, Epidemic spread modeling,
• Small-World
• Community Detection Problem
• Link Prediction Problem
• Evolving Network and Temporal Network
• Python NetworkX

Learning Materials

Textbook

• Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg, (Cambridge University Press - Sep 2010) - Prepublication draft available online. http://www.cs.cornell.edu/home/kleinber/networks-book/
• Network Science, by Albert-Laszlo Barabasi, (Cambridge University Press - August 2016) freely available under the Creative Commons licence. http://www.networksciencebook.com/
• Networks, by Mark Newman, (Oxford University Press, 2nd-edition - Sep 2018)

Reference Books

• Complex and Adaptive Dynamical Systems, by Claudius Gros, (Springer, 4th Edition - 2015).
• The Structure of Complex Networks Theory and Applications, by Ernesto Estrada, (Oxford University Press - Dec 2011).
• Exploratory Social Network Analysis with Pajek, by Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj, (Cambridge University Press, 3rd Edition - July 2018)

Self Learning Material

• https://www.barabasilab.com/course
• https://nptel.ac.in/courses/106106169/#

Grading Policy

Interaction

• Moodle Interactive Video
• Discussion Forum Activity
• Class Questions and Answer

Quizzes (Online/Offline)

• There will be about 4/5 quizzes
• Students in the campus will write in offline mode
• Students not in the campus will write online
• Students are expected to attend at least one offline quiz
• https://www.classmarker.com/ or other similar platform
• Weight of offline and online will be as per the academic guidelines
• Anyone missing offline quiz will be taken an makeup Viva at the end

Coding Assignments

• About one per week
• Preferably with NetworkX in Python

Project

• Report: 25%, Peer-Review: 25%, Presentation: 15%, Project Work: 35%
• Grading will be based on:
  • Organization: Applicable to Reports and Presentations
  • Clarity: Applicable to Reports/Presentation/Work/Interaction
  • Length: Too Short/Too Long will be penalized (report/presentation)
  • References: Correct and Comprehensive References
  • Correctness: Applicable to Development and Contributory Work
  • Significance/Coverage: Significance for Contributory Work, Coverage for Survey/Review
  • Originality: Plagiarized Content of any form (textual, formulation, images, results) will mark zero for the project component.
  • Contribution: Quality of Contribution
• Consideration for A* Grade: Paper submission to above average conference (at least)