# Embedding Multiple Graphs¶

This demo shows you how to simultaneously embed two graphs using omnibus embedding from two graphs sampled from different stochastic block model (SBM). We will also compare the results to that of adjacency spectral embedding, and show why it is useful to embed the graphs simultaneously.

[1]:

import graspy

import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


## Simulate two different graphs using stochastic block models (SBM)¶

We sample 2-block SBMs (undirected, no self-loops) with 50 vertices, each block containing 25 vertices (n = [25, 25]), and the following block probabilities:

\begin{align*} P_1 = \begin{bmatrix}0.2 & 0.1\\ 0.1 & 0.7 \end{bmatrix},~ P_2 = \begin{bmatrix}0.2 & 0.1\\ 0.1 & 0.2 \end{bmatrix} \end{align*}

The only difference between the two are the block probability for the second block. We sample $$G_1 \sim \text{SBM}(n, P_1)$$ and $$G_2 \sim \text{SBM}(n, P_2)$$.

[2]:

from graspy.simulations import sbm

n = [25, 25]
P1 = [[.3, .1],
[.1, .7]]
P2 = [[.3, .1],
[.1, .3]]

np.random.seed(8)
G1 = sbm(n, P1)
G2 = sbm(n, P2)


## Visualize the graphs using heatmap¶

We visualize the sampled graphs using heatmap function. Heatmap will plot the adjacency matrix, where the colors represent the weight of the edge. In this case, we have binary graphs so the values will be either 0 or 1.

There are clear block structure to the graphs, and we see that the second, lower right, block of $$G_1$$ has more edges than that of $$G_2$$.

[3]:

from graspy.plot import heatmap

heatmap(G1, figsize=(7, 7), title='Visualization of Graph 1')
heatmap(G2, figsize=(7, 7), title='Visualization of Graph 2')

[3]:

<matplotlib.axes._subplots.AxesSubplot at 0x153e52bad7f0>


## Embed the two graphs using omnibus embedding¶

Purpose of embedding graphs is to obtain a Euclidean representation, or sometimes called latent positions, of the adjacency matrices. Again, we assume that the probability matrix of a graph is given by $$P = XX^T$$ and we are trying to estimate $$X$$. The benefit of omnibus embedding is that the latent positions of all embedded graphs live in the have same canonical space, thus eliminating the need to align the results.

We use all of the default parameters. Underneath, the select_dimension algorithm will automatically find the optimal embedding dimension for us. In this example, we get the following estimate,

\begin{align*} \hat{Z} = \begin{bmatrix} \hat{X_1}\\ \hat{X_2} \end{bmatrix} \end{align*}

where the first block, $$\hat{X_1}$$ are the latent positions of the first graph, and $$\hat{X_2}$$ are the latent position of the second graph.

[4]:

from graspy.embed import OmnibusEmbed

embedder = OmnibusEmbed()
Zhat = embedder.fit_transform([G1, G2])

print(Zhat.shape)

(2, 50, 2)


## Visualize the latent positions¶

Since the two graphs have clear block structures, we should see two “clusters” when we visualize the latent positions. The vertices that form the first block should be close together since they have the same block probabilities, while those that form the second block should be further apart since they have different block probabilities.

[5]:

Xhat1 = Zhat[0]
Xhat2 = Zhat[1]

# Plot the points
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(Xhat1[:25, 0], Xhat1[:25, 1], marker='s', c='blue', label = 'Graph 1, Block 1')
ax.scatter(Xhat1[25:, 0], Xhat1[25:, 1], marker='o', c='blue', label = 'Graph 1, Block 2')
ax.scatter(Xhat2[:25, 0], Xhat2[:25, 1], marker='s', c='red', label = 'Graph 2, Block 1')
ax.scatter(Xhat2[25:, 0], Xhat2[25:, 1], marker='o', c='red', label= 'Graph 2, Block 2')
ax.legend()

# Plot lines between matched pairs of points
for i in range(50):
ax.plot([Xhat1[i, 0], Xhat2[i, 0]], [Xhat1[i, 1], Xhat2[i, 1]], 'black', alpha = 0.15)

ax.set_title('Latent Positions from Omnibus Embedding', fontsize=20)

[5]:

Text(0.5, 1.0, 'Latent Positions from Omnibus Embedding')