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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.


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)

Taught by

Sudarshan Iyengar

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