This Repository consist of the Solution of the SNA_Project

Data sets: http://konect.uni-koblenz.de https://snap.stanford.edu/data/ Web links in 1 and 2 have several datasets. If the first first link is not working please try this: http://konect.cc/

You can choose two datasets from the above options.

Once you fix the datasets you have to SOLVE TWO PROBLEMS for both the two data sets separately:

Find all centrality measures, clustering coefficients (both local and global) and reciprocity and transitivity that we have studied in the class using appropriate algorithms (you may use specific packages for this or write your own algorithm for the same). Try to get an algorithm package in Python to find the maximum connected component (called as giant component in the class) in a given graph G. Let us denote the number of nodes in the giant component of a graph G as N_G. Vary ⟨k⟩ from 0 to 5 with increment of 0.1. For each value of ⟨k⟩ find the ratio N_G/N where N is the number of nodes in the graph. Plot this ratio with respect to ⟨k⟩. Take ⟨k⟩ as x-axis and ratio N_G/N as y-axis. Produce a project report with all details like the

Complete Description of the data set, What is the network about, What are the centrality measures you studied in the data set, What are the clustering coefficients (local and global) for this data set, Which are all (centrality measures) appropriate for your study, What is the output What is your inferences from the output and so on What do you infer from the plot of the problem (2) does it go well with what we had studied in Barabasi book on Random networks. What are the four regimes (subcritical, critical point, super critical and connected) that you get for your graph? You need to submit this report with all details by March 20, 2021 midnight through Moodle ONLY.

If there is any doubt you may ask your questions here in the same thread. Please do not email or open a new thread for this.