Gridplot: Visualize Multiple Graphs¶
This example provides how to visualize graphs using the gridplot.
[1]:
import graspologic
import numpy as np
%matplotlib inline
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
/home/runner/work/graspologic/graspologic/graspologic/models/edge_swaps.py:215: NumbaDeprecationWarning: The keyword argument 'nopython=False' was supplied. From Numba 0.59.0 the default is being changed to True and use of 'nopython=False' will raise a warning as the argument will have no effect. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.
_edge_swap_numba = nb.jit(_edge_swap, nopython=False)
Overlaying two sparse graphs using gridplot¶
Simulate more graphs using weighted stochastic block models¶
The 2-block model is defined as below:
\begin{align*} P = \begin{bmatrix}0.25 & 0.05 \\ 0.05 & 0.25 \end{bmatrix} \end{align*}
We generate two weighted SBMs where the weights are distributed from a discrete uniform(1, 10) and discrete uniform(2, 5).
[2]:
from graspologic.simulations import sbm
n_communities = [50, 50]
p = np.array([[0.25, 0.05], [0.05, 0.25]])
wt = np.random.randint
wtargs = dict(low=1, high=10)
np.random.seed(1)
A_unif1= sbm(n_communities, p, wt=wt, wtargs=wtargs)
wtargs = dict(low=2, high=5)
A_unif2= sbm(n_communities, p, wt=wt, wtargs=wtargs)
Visualizing both graphs¶
[3]:
from graspologic.plot import gridplot
X = [A_unif1, A_unif2]
labels = ["Uniform(1, 10)", "Uniform(2, 5)"]
f = gridplot(X=X,
labels=labels,
title='Two Weighted Stochastic Block Models',
height=12,
font_scale=1.5)