<｜begin▁of▁sentence｜>### Instruction:
As a network analyst studying coauthorships in the network science field, you have a dataset in the form of a GML file named netscience.gml, containing information about the relationships between authors. You want to use the Louvain and Walktrap methods in cdlib to identify communities within this network and analyze the relation between community size and internal edge density. This will help you gain insights into how collaborations form within the network science community and how tightly-knit these collaborative communities are.
### Response:
from cdlib import algorithms, viz, evaluation
import networkx as nx
# Read the network science coauthorship graph
g = nx.read_gml('netscience.gml')
# Detect communities using the Louvain method
coms = algorithms.louvain(g)
# Detect communities using the Walktrap method
coms2 = algorithms.walktrap(g)
# Plot the relation between community size and internal edge density
lmplot = viz.plot_com_properties_relation([coms,coms2], evaluation.size, evaluation.internal_edge_density)
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