network models
Budget: £10 – £20 GBP
1) The ER model
The aim here is to investigate how the ER model works by examining its degree distribution, the average path length and the clustering coefficient.
Task 1.1 Use the ER model (script_ER_single_null.m) to generate a single random network and an ensemble of 100 networks with p = 0.01. Set the number of nodes,
n = 100.
a) Provide the graphs you have produced from the Matlab code here. Compare the degree distribution, the average path length and the clustering coefficient of a single ER network with the averages obtained from the ensemble of networks.
b) Based on the results you have obtained, explain why an ensemble of networks is often used when examining networks generated by a stochastic process.
The aim here is to investigate how the ER model works by examining its degree distribution, the average path length and the clustering coefficient.
Task 1.1 Use the ER model (script_ER_single_null.m) to generate a single random network and an ensemble of 100 networks with p = 0.01. Set the number of nodes,
n = 100.
a) Provide the graphs you have produced from the Matlab code here. Compare the degree distribution, the average path length and the clustering coefficient of a single ER network with the averages obtained from the ensemble of networks.
b) Based on the results you have obtained, explain why an ensemble of networks is often used when examining networks generated by a stochastic process.