<｜begin▁of▁sentence｜>### Instruction:
As a home inspector, imagine you have been hired to evaluate a large property where the owner suspects that there might be hidden structural issues. In order to assess the property thoroughly, you plan to use a specialized tool called community_walktrap provided by the igraph API to detect potential clusters of structural weaknesses within the property. After detecting these clusters, you aim to visually represent the property's structure by plotting the network using colors to indicate the identified clusters.
### Response:
import igraph as ig

# Load a graph from a file (this file should contain a valid graph)
g = ig.Graph.Read('network.gml')

# Optionally, if the graph has "weight" attribute for edges or you want to assign your own weights:
# weight = g.es['weight'] # Use this if the weights are already part of the graph's edge attributes
# weight = [1, 2, 3, ...] # Or assign a list of weights manually

# Execute the community_walktrap algorithm with a specified step count
communities = g.community_walktrap(weights='weight', steps=4)

# Convert the resulting dendrogram into an actual clustering object
classification = communities.as_clustering()

# Print the modularity of the clustering
print('Modularity:', classification.modularity)

# Plot the graph with vertex colors based on the clustering
ig.plot(classification, vertex_label=g.vs['name'])
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