# Network Models: Random Graphs¶

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import numpy as np
import matplotlib.pyplot as plt
plt.xkcd()
import networkx as nx
%matplotlib inline


### Clustering coefficient¶

During the lecture we have understood, that the clustering coefficient of a random graph is equal to the probability $p$: $$\text{Clustering coefficient} = \frac{\langle k \rangle}{n} = p$$

In this task you have to check it on generated data. Please, generate $100$ Random Graphs with $n = 1000$ and $p = 0.002$ (for saving computational time) and plot the box-plot of your computations.

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# Put your code here


### Size of small components¶

In this task you are asked to calculate the average size of small components (small component = not a giant one) with regard to average degree of the network. To see the effect clearly, plot average size around $\langle k \rangle = 1$.

In [ ]:
# Put your code here