<｜begin▁of▁sentence｜>### Instruction:
Suppose you have two graphs generated using the ErdÅ‘s-RÃ©nyi model, each with 5 nodes and an edge creation probability of 0.5. These two graphs represent different types of interactions within the same set of entities. How would you use the graspologic 'gridplot' function to visualize these graphs together with log-transformed edge weights, while ensuring that the nodes are sorted according to the sum of their edge weights?
### Response:
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
from graspologic.plot import gridplot

# Generate two synthetic graphs using the ErdÅ‘s-RÃ©nyi model
G1 = nx.erdos_renyi_graph(n=5, p=0.5)
G2 = nx.erdos_renyi_graph(n=5, p=0.5)

# Create a list of adjacency matrices
adjacency_matrices = [nx.to_numpy_array(G1), nx.to_numpy_array(G2)]

# Specify labels for the graphs
labels = ['G1', 'G2']

# Plotting the gridplot with specific parameters
gplot = gridplot(
    X=adjacency_matrices,
    labels=labels,
    transform='log',
    height=10,
    title='Multiple Graphs',
    context='talk',
    font_scale=1.25,
    palette='deep',
    alpha=0.7,
    sizes=(10, 200),
    legend_name='Edge Weight',
    inner_hier_labels=None,
    outer_hier_labels=None,
    hier_label_fontsize=10,
    title_pad=None,
    sort_nodes=True
)

plt.show()
<|EOT|>
