<｜begin▁of▁sentence｜>### Instruction:
Given random latent positions for vertices in a graph, can you generate a pair of correlated random dot product graphs (RDPGs) with specified correlation, and convert them into networkx graph objects for further analysis?
### Response:
import numpy as np
from graspologic.simulations import rdpg_corr

# Seed for reproducibility
np.random.seed(123)

# Generate random latent positions using 2-dimensional Dirichlet distribution
X = np.random.dirichlet([1, 1], size=5)

# Sample a correlated RDPG graph pair
G1, G2 = rdpg_corr(X, X, 0.3, rescale=False, directed=False, loops=True)

# Convert the probabilities to a binary graph representation
G1_binary = (G1 >= 0.5).astype(int)
G2_binary = (G2 >= 0.5).astype(int)

# Use NetworkX to convert the adjacency matrices to graph objects for analysis
import networkx as nx
G1_nx = nx.from_numpy_array(G1_binary)
G2_nx = nx.from_numpy_array(G2_binary)
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