<｜begin▁of▁sentence｜>### Instruction:
As a jewelry appraiser, you might be tasked with evaluating a collection of gemstones and precious metals that have been arranged into a network of connections. Suppose you have received a graph representing these connections in GraphML format, named 'gemstone_network.graphml'. Your task is to apply the community_fastgreedy function from the igraph library to detect the structures within the gemstone network and then use NetworkX to visualize these community structures.
### Response:
import igraph as ig
import networkx as nx
import matplotlib.pyplot as plt

# Read the graph (Assuming the GraphML format for the example)
g = ig.Graph.Read_GraphML('graph_data.graphml')

# Calculate the community structure using the fastgreedy algorithm
fs_dendrogram = g.community_fastgreedy()
clusters = fs_dendrogram.as_clustering()

# Convert to NetworkX for visualization (optional, since igraph has its own visualization tools)
nx_graph = nx.read_graphml('graph_data.graphml')
nx.draw(nx_graph, with_labels=True)
plt.show()
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