CSL7390: Social Network Analysis

Offered in Autumn 2021 Semester

Offered in Autumn 2021 Semester

  • 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: O (Monday 6:00 PM - 7:30 PM, Saturday 12:00 PM - 1:00 PM)
  • LMS: Moodle

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, Barab├ísi-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

Grading Policy

Interaction Quizzes (Online/Offline) Project
5% 5%+10% 30%
Coding Assignment Major (Open-book/equivalent) Major (Online)
10% 20% 20%

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)
Project will be in groups. Size of the group will be decided after add/drop, i.e., Around August 15, 2021.

Important Dates:

  • 23/Aug: Last date of group identification
  • 31/Aug: Last date for Topic identification
  • 01/Sep: 2 Slide Presentation on Motivation
  • 06/Sep: ^^ if necessary
  • 13/Oct: Abstract Submission
  • 31/Oct: Report Submission
  • 01/Nov - 10/Nov: Peer Review
  • 17/Nov: Revision Submission
  • Presentation dates will be announced (tentative: Nov 1, 6, 8, 13)
    • 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