<｜begin▁of▁sentence｜>### Instruction:
Imagine you want to model a social network where each pair of individuals has a 50% chance of knowing each other. You decide to use an Erdos Renyi graph to create this network model. The network should consist of 10 individuals. How would you use graspologic to generate the adjacency matrix of such a network and convert it to a networkx graph to analyze its properties?
### Response:
import numpy as np
from graspologic.simulations import er_np
import networkx as nx

# Set the random seed for reproducibility
np.random.seed(42)

# Define the number of vertices and the probability of an edge
n = 10
p = 0.5

# Generate a binary Erdos Renyi graph
A = er_np(n, p)

# Convert the adjacency matrix to a networkx graph
G = nx.from_numpy_matrix(A)

# Print information about the generated graph
print(nx.info(G))
<|EOT|>
