<｜begin▁of▁sentence｜>### Instruction:
Imagine we're gearing up for a big game in the world of network analysis. In one corner, we have two contenders created by the fabled coach Erdős-Rényi—let's call them Team ER1 and Team ER2. These squads were built using the classic random connection playbook. In the other corner, there's a modified team, Team Modified, which has been strategically tweaked to include a powerhouse duo, a pair of nodes with a particularly strong connection that could be a game changer.

The analyst, acting as the coach, is looking to review the playbooks of these three teams—their edge weight distributions, to be precise—before the big showdown. Now, to get a bird's-eye view of the squads' strategies, the analyst wants to put their playbooks side by side using the 'gridplot' feature from the playbook of 'graspologic.plot'. This high-level strategy session is designed to look at the distribution of passes between players—the edges—across the teams for a tactical comparison.

The call of the game here is to make use of the 'gridplot' function for a strategic combined visualization, allowing a direct comparison of how the ball, or in this case, information, might flow through each team. And just like adjusting the strategy for different opponents, the analyst has a variety of options to customize the playbook views, to make sure they get the clearest picture of the team dynamics before the main event.

So, with the 'gridplot' function from 'graspologic.plot' as the analyst's strategy board, it's all about preparing to visualize and compare the edge-weight game plans of Team ER1, Team ER2, and Team Modified in one comprehensive view. What moves will the analyst make to set up this crucial pre-game analysis? Remember folks, in the realm of network analysis, the best offense is a good defense, and a clear visualization of your team's edge weights just might be the best defense there is.
### Response:
import networkx as nx
import numpy as np
from graspologic.plot import gridplot

# Create three synthetic graphs with some similarities
G1 = nx.erdos_renyi_graph(n=10, p=0.5)
G2 = nx.erdos_renyi_graph(n=10, p=0.5)
G3 = nx.erdos_renyi_graph(n=10, p=0.5)

# Tweak G3 to have a strong connection between node 0 and node 9
G3.add_edge(0, 9, weight=5)

# Convert graphs to adjacency matrices
A1 = nx.to_numpy_array(G1)
A2 = nx.to_numpy_array(G2)
A3 = nx.to_numpy_array(G3)

# Create a list of adjacency matrices and labels for plotting
X = [A1, A2, A3]
labels = ['ER Graph 1', 'ER Graph 2', 'Strong Edge Graph']

# Call gridplot with the list of adjacency matrices and labels
gridplot(X=X, labels=labels, title='Gridplot of ER Graphs with a Strong Edge Added', font_scale=1.2, palette='viridis', alpha=0.9, sizes=(10, 200))
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