Network Science

Code: MA653 | L-T-P-C: 3-0-0-6

Prerequisites: MA 512 Data Structures & Algorithms or equivalent

Preamble/Objectives: Network science is the study of topology and dynamics of complex networks with an aim to understand the behavior, function and properties of these systems. The applications of network science include informational, social, biological and cognitive networks. The course will investigate the. algorithmic and computational methods of network science as well as its application in real networks.

Course Content/ Syllabus: Relevant concepts from graph theory - path, connected component, random walk, network types;   Network analysis metrics - clustering, degree correlations, centrality, social network analysis methods. Properties of networks - scale-free, small world. Network evolution models - random networks, preferential attachment models and its variants; Watts & Strogatz model. Community detection methods and real world application of community detection. Dynamics on networks - percolation, spreading, synchronization and real world applications. Network analysis tools - Pajek, iGraph.

References:

1. M.E.J. Newman, Networks - An introduction, 1st Ed,, Oxford Univ. Press, 2010.
2. Albert-Laszlo Barabasi, Network Science, 1st Ed, Cambridge Univ. Press, 2016
3. R. Cohen and S. Havlin, Complex Networks - Structure, Robustness and Function, Cambridge Univ Press, 2010
4. D. Easley and J. Kleinberg, Networks, Crowds and Markets:  Reasoning About a Highly Connected World, Cambridge Univ. Press, 2010