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.
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.)".
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.
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.
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.
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?
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
4813 students have enrolled already!!
COURSE INSTRUCTOR
Sudarshan Iyengarhas 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. TEACHING ASSISTANTS
COURSE LAYOUT:
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)
SUGGESTED READING: 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.
CERTIFICATION EXAM:
1.The exam is optional for a fee.
2.Date and Time of Exams: April 28 (Saturday) and April 29 (Sunday) : Morning session 9am to 12 noon;
3.Exam for this course will be available in one session on both 28 and 29 April.
4.Registration url: Announcements will be made when the registration form is open for registrations.
5.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.
CERTIFICATE:
The criteria for certification in this course is different because of the online programming exam component. Please read the following carefully:
Proctored exam (to be attended in person): 65% weightage, Date: 28/29 April 2018.
IMPORTANT:
To pass the course and get a certificate: Final score >= 40/100 To get an Elite category of certificate: Final score >= 60/100
To get a gold medal stamp in the certificate: Final score >= 90/100
The certificate willhave your name, photograph and the score in the final exam with the breakup. It will have the logos of NPTEL and Indian Institute of Technology, Madras. It will be e-verifiable at nptel.ac.in/noc.
