Social Network Analysis

By Prof. Tanmoy Chakraborty   |   IIIT Delhi
Learners enrolled: 3826
Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with the emergence of online social networks and large-scale data availability in social sciences, this course focuses on the analysis of massive networks which provide many computational, algorithmic, and modeling challenges. The course will cover research on the structure and analysis of such large networks and on models and algorithms that abstract their basic properties. We will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics covered in this course are how information spreads through society; robustness and fragility of networks; algorithms for the World Wide Web; prediction and recommendation in online social networks; representation learning for large networks; etc.

PREREQUISITES: Python programming, Probability and Statistics, Machine Learning

INDUSTRY SUPPORT: Any social media company, E-commerce company, etc
Course Status : Completed
Course Type : Elective
Duration : 12 weeks
Category :
  • Computer Science and Engineering
Credit Points : 3
Level : Undergraduate/Postgraduate
Start Date : 25 Jul 2022
End Date : 14 Oct 2022
Enrollment Ends : 08 Aug 2022
Exam Date : 29 Oct 2022 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction ; Tutorial 1: Introduction to Python/Colab ; Tutorial 2: Introduction to NetworkX - Part I 
Week 2: Network Measures ; Tutorial 3: Introduction to NetworkX - Part II 
Week 3: Network Growth Models
Week 4: Link Analysis
Week 5: Tutorial 4: Graph Visualization Tools ; Community Detection - Part I
Week 6: Community Detection - Part II
Week 7: Link Prediction
Week 8: Cascade Behavior and Network Effects
Week 9: Anomaly Detection
Week 10: Introduction to Deep Learning ; Graph Representation Learning - Part I
Week 11: Graph Representation Learning - Part II ; Tutorial: Coding on Graph Representation Learning
Week 12: Applications and Case Studies ; Conclusion

Books and references

  1. Social Network Analysis, Tanmoy Chakraborty, Wiley, 2021
  2. Network Science, Albert-Lazzlo Barabasi
  3. Social Network Analysis: Methods and Applications, Stanley Wasserman, Katherine Faus

Instructor bio

Prof. Tanmoy Chakraborty

IIIT Delhi
Prof. Tanmoy is an associate professor of computer science and a Ramanujan Fellow at IIIT Delhi where he leads a research group, LCS2. He is also heading the Infosys Center for AI at IIIT Delhi. His group broadly works in the areas of NLP and Graph Mining, with a major focus on building machine learning models for cyber-crime and cybersafety. Tanmoy did his PhD from IIT Kharagpur in 2015 as a Google PhD scholar and worked at the University of Maryland, College Park as a postdoctoral scholar before joining IIITD in 2017. He also works on designing lightweight and explainable models for language understanding and graph processing. He has recently authored a textbook on social network analysis. More details: http://faculty.iiitd.ac.in/~tanmoy/

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 29 October 2022 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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