<｜begin▁of▁sentence｜>### Instruction:
Given a graph generated by the LFR benchmark algorithm with 250 nodes, where the mixing parameter is set to 0.1, average degree 5, minimum community size 20, power exponent for the degree sequence is 3, power exponent for the community size distribution is 1.5 and seed of 10, can you use EgoNetSplitter to find the overlapping community structure in this network and provide the cluster memberships of the nodes?
### Response:
import networkx as nx
from karateclub import EgoNetSplitter

# Create a small graph with overlapping communities
g = nx.lfr_benchmark_graph(250, 3, 1.5, 0.1, average_degree=5, min_community=20, seed=10)

# Instantiate the EgoNetSplitter model with default parameters
model = EgoNetSplitter()

# Fit the model to the graph
g = nx.convert_node_labels_to_integers(g)
model.fit(g)

# Get the cluster membership of nodes
memberships = model.get_memberships()
print(memberships)
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