<｜begin▁of▁sentence｜>### Instruction:
As a legal secretary assisting a team of legal professionals in a bustling law firm, you've been tasked with analyzing the social dynamics within a prominent legal case involving multiple parties. You have access to a dataset representing the connections between various individuals involved in the case, stored in a GML file named 'case_network.gml'. Your objective is to apply advanced network analysis techniques to identify distinct communities within this network.

Utilizing the Louvain method available through the Community Detection Library (cdlib), you are to perform community detection on the provided network data. Additionally, you are required to visualize the relationship between the size of these communities and their internal edge density, offering insights into the clustering tendencies among the involved parties.

Finally, you're instructed to save the resulting plot as 'community_size_vs_internal_density.png', providing a clear visual representation for further analysis and presentation within the legal proceedings.
### Response:
from cdlib import algorithms, viz, evaluation
import networkx as nx
G = nx.read_gml('lesmis.gml')
coms = algorithms.louvain(G)
lmplot = viz.plot_com_properties_relation(coms, evaluation.size, evaluation.internal_edge_density)
lmplot.savefig('community_size_vs_internal_density.png')
<|EOT|>
