CSL7390: Social Network Analysis (Spring 2024)

Table of Contents
  • Credits L-T-P [C]: 3-0-0 [3]
  • Expectation from 7000 level course:
    • 1 Contact Hr + 3 Non-Contact Hr
    • Learn by Research
  • Where: Google Meet (Link Available in Moodle)
  • Slot: YR2 (Friday and Saturday 6:00 PM - 7:30 PM)
  • LMS: Moodle
    • Easy enrollment code: fn8gkq


  • 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


  • 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

Grading Policy

Interaction/Quizzes Coding Assignment Mini Project
10% 15% 20%
Paper Implementation Major
15% 40%


  • Each class will have 2 to 3 quizzes on slido. On mobile or laptop.
  • Questions will be on understanding level
  • 75% of the quizzes will be part of the grading
  • Leader board will be shown each class

Coding Assignments

  • About one per week
  • Preferably with NetworkX in Python


  • 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)
Project will be in groups. Maximum size of a group will be 4 members.

Important Dates:

  • 23/Jan: Last date of group identification
  • 15/Feb: Last date for Topic identification
  • 16-17/Feb: 2 min Presentation on Motivation
  • 16/Mar: Abstract Submission
  • 30/Mar: Report Submission
  • 1/Apr - 10/Apr: Peer Review
  • 17/Apr: Revision Submission
  • Presentation dates will be announced (tentative: Apr 12, 13, 19, 20)
    • Video Presentation if 30+ project is there (dates will be announced)

Plagiarism tolerance is 7% from single source and 15% cumulative, anything more will reduce your marks as follows:

  • Any logical/conceptual/formulation plagiarism: zero marks
  • Other form of plagiarism (above 50%): zero marks
  • Otherwise: Percentage of plagiarism will be deducted from the obtained mark