<｜begin▁of▁sentence｜>### Instruction:
As a network analyst investigating potential cyber threats, you may encounter multiple instances of graphs generated using the Erdos-Renyi model with 50 nodes and an edge creation probability of 0.1. To extract valuable insights and identify patterns from these graphs, you may consider utilizing the Invariant Graph Embedding model to generate embeddings for each graph. This can help in detecting similarities or anomalies across the network structures, ultimately aiding in the identification of potential cyber threats.
### Response:
import networkx as nx
from karateclub import IGE

# Creating a list of graphs
G1 = nx.erdos_renyi_graph(50, 0.1)
G2 = nx.erdos_renyi_graph(50, 0.1)
graphs = [G1, G2]

# Initializing the IGE model with default parameters
model = IGE()

# Fitting the IGE model with the list of graphs
model.fit(graphs)

# Getting the embeddings for the graphs
embeddings = model.get_embedding()

print(embeddings)
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