Calculate the mcf of a generated random graph

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Reference no: EM131403281

This project tests the observation that the mean clustering coefficient (MCF) for networks exhibiting the small-world property, such as most social net-works, is significantly higher than for random graphs (Mason and Verwoerd 2007). Using a realistic social network data set, calculate the MCF and the percentage (p) of Is, indicating edges, in its people-people connection ma-trix. Develop a function with parameters for a size, n, and a probability, p, to return a random n x n connection matrix, where p is the probability of 1 in a position. Calculate the MCF of a generated random graph for the number of people and the percentage of ones in the realistic social network. Run this simulation a number of times, say 100 to 1000 times, to obtain an average value for MCF, and compare the results with that of the realistic social network.

Reference no: EM131403281

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