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Social networks


The world has become highly interconnected and hence more complex than ever before. We are surrounded by a multitude of networks in our daily life, for example, friendship networks, online social networks, world wide web, road networks etc. All these networks are today available online in the form of graphs which hold a whole lot of hidden information. They encompass surprising secrets which have been time and again revealed with the help of tools like graph theory, sociology, game theory etc. The study of these graphs and revelation of their properties with these tools have been termed as Social Network Analysis.
Some of the surprising observations and beautiful discoveries achieved with Social Network Analysis are listed below.
  1. 6 degrees of separation: You can reach out to any person on this earth within an average of 6 hops. That means, "You know someone who knows someone who knows someone who knows someone who knows someone who knows Justin Beiber (or Angelina Jolie or literally anyone on this planet.)".
  2. The algorithm behind Google search: How does Google achieve such precise and valid search results? The underlying algorithm is fairly simple and relies totally on the network of web pages.
  3. How do you get your dream job: Not through your best friends but through your acquaintances to whom you talk relatively less frequently! Sounds counterintuitive.
  4. Link prediction: Can one predict who is going to be your next Facebook friend, or which product are you going to buy next on Flipkart, or which is the next movie you are going to watch on Netflix? Yes, it is possible.
  5. Viral Marketing: Want to make your new product sell out quickly? How do you determine the people to whom you should be giving the free samples? Does that even matter? 
  6. Contagion: Not only information but happiness, obesity, altruism, depression all spread from person to person.

As one can see through above examples, the study of networks has penetrated into all spheres of our life. The course revolves around the study of some well-known theories of social and information networks and their applications on real-world datasets. Not only does the course introduces you to the current advancement in the field, but paves a way for you to take this advancement one step further.

Moreover, the course is highly programming intensive. Not to worry, we do not assume the students to know Python before hand and provide even the basic tutorials for this language. Hence, it is also a great way to learn this powerful programming language. The course takes you from the most basic functionality of Python to the most advanced one where the students are able to  code a real word dataset crunching algorithm on their own.

By the end of the course, you will
  • be well versed in the basic theories and popular results of social network analysis.
  • be able to crunch the online available graph datasets and process them with the help of python networkx package.
  • be able to visualize the graph datasets.

Towards the end of the course, a couple of ongoing research projects in this area will also be discussed. We also aim at providing the top scorers an opportunity to collaborate with us. So, please do write to us if you are interested to pursue research in this area.

PRE-REQUISITES: The course doesn’t assume any pre-requisites. We expect one has undergone a first course in basic programming.

INDUSTRY SUPPORT: This is a much sought after field in computer science and many industries value/recognize this course. Today, social network analysis in being employed in private as well as public sectors. Some of the areas where it is used are
  • Modeling the Networks of Organizations
  • Understanding Customer Interaction
  • Development of Information Systems
  • Digital Marketing
  • Risk Management
  • Banking
  • Telecommunication Analytics
  • Bioinformatics
  • Criminal Intelligence
  • Human Resources Development
  • Designing Leader Engagement Strategies
  • Community based Problem Solving
  • Knowledge Management

7540 students have enrolled already!!


Sudarshan Iyengar has a Ph.D. from the Indian Institute of Science and is currently working as an assistant professor at IIT Ropar and has been teaching this course from the past 5 years. Apart from this course, he has offered several other courses in IIT Ropar like Discrete Mathematics, Theory of Computation, Cryptography, Probability and Computing etc. His research interests include social networks, crowdscoured knowledge building and computational social sciences. His current research proects are "Predicting a Viral meme" (Yayati Gupta), "Understanding Crowdsourced Knowledge buidling" (Anamika Chhabra - Scientist), "Secure Computation" (Varsha Bhat) and "Network Sampling" (Akrati Saxena).

After research, teaching makes the major component of his academic life. He enjoys experimenting with different teaching methodologies. He particularly enjoys traveling and giving talks on his research work apart from motivational talks of popsci genre.

Rishi Ranjan Singh is an Assistant Professor in the Department of Electrical Engineering and Computer Science at IIT Bhilai. He has held assistant professor and visiting faculty positions in the Department of Information Technology at IIIT Allahabad. He received his Ph.D. from the Department of Computer Science and Engineering at IIT Ropar in March 2016. He received his B. Tech (Hons.) in Computer Science and Engineering from UPTU Lucknow, India in 2011. His research interests are in the area of Social & Complex Network Analysis, Algorithms, Combinatorial Optimization, Mathematical Formulation and Vehicle Routing Problems.


Week 1: Introduction 
Week 2: Handling Real-world Network Datasets
Week 3: 
Strength of Weak Ties
Week 4: 
Strong and Weak Relationships (Continued) & Homophily 
Week 5: 
Homophily Continued and +Ve / -Ve Relationships 
Week 6: 
Link Analysis 
Week 7:
 Cascading Behaviour in Networks
Week 8:
 Link Analysis (Continued) 
Week 9:
 Power Laws and Rich-Get-Richer Phenomena 
Week 10: 
Power law (contd..) and Epidemics 
Week 11: 
Small World Phenomenon
Week 12: Pseudocore (How to go viral on web) 


1. Networks, Crowds and Markets by David Easley and Jon Kleinberg, Cambridge University Press, 2010
(available for free download).
2. Social and Economic Networks by Matthew O. Jackson, Princeton University Press, 2010.

  • The exam is optional for a fee.
  • Date of Exam: April 28th 2019 (Sunday).
  • Time of Exam: Morning session 9am to 2 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.

  • Final score will be calculated as : 25% assignment score + 75% final exam score
  • 25% assignment score is calculated as 25% of average of  Best 8 out of 12 assignments
  • E-Certificate will be given to those who register and write the exam and score greater than or equal to 40% final score. 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.