<｜begin▁of▁sentence｜>### Instruction:
As a Patent Attorney, you often assist clients in navigating the complex landscape of intellectual property rights to safeguard their innovations and foster economic growth. Imagine a scenario where you're working with a client in the field of high-energy theory collaborations. Your client has amassed a significant network of collaborations, documented in a GML file named "hep-th.gml." They seek your expertise to analyze this network and identify cohesive communities within it.

To address this, you'll utilize the Louvain algorithm, renowned for its efficacy in community detection within networks. Your objective is to partition the collaboration network into distinct communities, revealing underlying structures and relationships among researchers and institutions.

Once you've applied the Louvain algorithm to identify these communities, your next step is to visualize the results for clear interpretation. Leveraging the capabilities of the cdlib library, specifically its plot_network_clusters function, you'll generate visualizations that highlight the delineated communities within the collaboration network. This visual representation will not only aid your client in understanding the network's dynamics but also assist in strategic decision-making regarding their intellectual property and collaborative endeavors.

By integrating your legal expertise with advanced network analysis techniques, you empower your client to make informed decisions, protect their intellectual assets, and contribute to the advancement of high-energy theory research.
### Response:
from cdlib import algorithms, viz
import networkx as nx
import matplotlib.pyplot as plt

# Read the graph from 'hep-th.gml'
G = nx.read_gml('graph/hep-th.gml')

# Perform community detection using the Louvain algorithm
communities = algorithms.louvain(G)

# Plot the detected communities with networkx spring layout
pos = nx.spring_layout(G)
viz.plot_network_clusters(G, communities, pos, figsize=(10, 10), node_size=100, plot_overlaps=False, plot_labels=False, cmap='viridis')
plt.show()
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