<｜begin▁of▁sentence｜>### Instruction:
Imagine you are working on a digital composition that represents two networks of interactions among a group of five models, with each interaction having an equal chance of occurring. These networks, akin to photographs, were created using a digital algorithm known as the Erdős-Rényi model, with each potential connection between the models having a 50% chance of being established.

In this scenario, you need to enhance the visual clarity of the networks by adjusting the visual 'weight' of each interaction. This involves a digital process similar to applying a log transformation to the connections' intensity to better distinguish the variation in interactions.

Your task is to arrange and showcase these networks side-by-side, employing a tool from the graspologic API called 'gridplot'. The focus should be to align the models in the composition based on their overall connectedness across both networks – in other words, you want to sort them by the sum of their connection weights.

How would you proceed with this digital arrangement within graspologic's 'gridplot' function to ensure that the models are presented in an orderly fashion that accurately reflects their prominence within the network's tapestry of interactions?
### 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|>
