<｜begin▁of▁sentence｜>### Instruction:
As a cultural anthropologist studying the social networks within different communities, you have collected data to represent the interactions between individuals and the events they attend. The data is stored in a file called 'bipartite_graph.gml', and each individual is designated as belonging to a specific social group using the 'type' attribute. You are interested in analyzing the relationships between individuals and the events they attend by projecting the bipartite graph into two one-mode graphs, while ensuring that the relationships and interactions are accurately preserved.

You can achieve this by using the 'bipartite_projection' method provided in the networkx library in Python. This method allows you to create two separate one-mode graphs representing the relationships between individuals and the relationships between events, while maintaining the detailed information about the number of interactions between individuals and events.
### Response:
from igraph import Graph

# Read a bipartite graph
bipartite_g = Graph.Read_GML('bipartite_graph.gml')

# Assume 'type' attribute defines the parts of the bipartite graph
# Project the bipartite graph into one-mode graphs, maintaining edge multiplicity
projection1, projection2 = bipartite_g.bipartite_projection(types='type', multiplicity=True)

# Print the summary of the projections
print('Projection 1 (type=0):', summary(projection1))
print('Projection 2 (type=1):', summary(projection2))
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