Network Models: Random Networks, Scale Free Networks, The Barabási-Albert Model, Erdos-Renyi Model
Structural Analysis of Networks using Python: Python for Network Analysis, Empirical Studies, Structural Properties, Generate Synthetic Networks, Working with signed networks
Social Network Applications: Information Cascades, Small-World Phenomenon, Epidemics, Community Detection, Link Prediction, Page Rank
Evolving Network and Temporal Networks: Network evolution, working with Temporal Network Data
Multiplex and Multi-layer network, Network Analysis in Biology, Sports, Transports
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)
As per the notification from academics 100% attendance is mandatory. If you have genuine reason please take leave approval as per academics rule.
If attendance falls below 75%, one should get at least C grade to pass the course. Otherwise F grade will be assigned.
Grading Policy
Interactive Videos
Quizzes (Moodle)
Special Assignment
10%
15%
15%
Coding Assignment
Minors
Major
15%
10% + 10%
25%
Interactive Video (Moodle)
All the topics in the syllabus is covered in interactive videos where all the videos are embedded with questions. Answer the questions and get the grades. You can try as many times as you wish and improve your scores.
Quizzes (Moodle)
There will be about 4-6 quizzes; best 3/4 will be considered for grading.
All the quizzes will be in Moodle Platform.
No makeup quiz will be taken considering there will be more than required no of quizzes.
Coding Assignments
There will be many coding assignments, about 8-10.
Least 2 scores will not be consider for grading.
Preferably with NetworkX in Python
Special Assignment
There will be one special assignment for which one may need to read papers and implement the code.
Quiz dates will be announced during class
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