Generate modified small-world networks and compare with theoretical predictions.

## Project description

Generate and analyze small-world networks according to the revised
Watts-Strogatz model where the randomization at *β* = 1 is truly equal to the Erdős-Rényi network model.

In the Watts-Strogatz model each node rewires its *k*/2
rightmost edges with probality *β*. This means that each node has halways
minimum degree *k*/2. Also, at *β* = 1, each edge has been rewired.
Hence the probability of it existing is smaller than *k*/(*N*-1), contrary to the ER model.

In the adjusted model, each pair of nodes is connected with a certain
connection probability. If the lattice distance between the potentially
connected nodes is d(i,j) <= *k*/2 then they are connected with
short-range probability p_S = k / (k + β (N-1-k)), otherwise they’re
connected with long-range probability p_L = β * p_S.

## Install

pip install smallworld

Beware: smallworld only works with Python 3!

## Example

In the following example you can see how to generate and draw according to the model described above.

```
from smallworld.draw import draw_network
from smallworld import get_smallworld_graph
import matplotlib.pyplot as pl
# define network parameters
N = 21
k_over_2 = 2
betas = [0, 0.025, 1.0]
labels = [ r'$\beta=0$', r'$\beta=0.025$', r'$\beta=1$']
focal_node = 0
fig, ax = pl.subplots(1,3,figsize=(9,3))
# scan beta values
for ib, beta in enumerate(betas):
# generate small-world graphs and draw
G = get_smallworld_graph(N, k_over_2, beta)
draw_network(G,k_over_2,focal_node=focal_node,ax=ax[ib])
ax[ib].set_title(labels[ib],fontsize=11)
# show
pl.subplots_adjust(wspace=0.3)
pl.show()
```

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