<｜begin▁of▁sentence｜>### Instruction:
As a costume designer, you might want to identify distinct groups of actors based on their shared interactions and connections. Imagine using the intricate patterns of interwoven relationships among actors to create unique costumes for each group. To achieve this, you can use the community_walktrap algorithm in the NetworkX Python library, with a walk length of 4 steps, to identify these clusters and create distinct costume designs for each group of actors. Once the algorithm has been applied, you can then visualize and print the resulting clusters to inform your costume design process.
### Response:
import igraph as ig

# Load a graph
filename = 'graph_network.gml'
g = ig.read(filename)

# Run community detection using the walktrap method
community_walktrap = g.community_walktrap(weights=None, steps=4)

# Convert the result to actual clusters
clusters = community_walktrap.as_clustering()

# Print the clusters
print(clusters)
<|EOT|>
