<｜begin▁of▁sentence｜>### Instruction:
As a virus researcher, you study the biology, transmission, and evolution of viruses, including infectious diseases, to develop vaccines, treatments, or preventive measures. Imagine you have collected data on interactions between individuals within a community to better understand the spread of a virus. Based on this data, you are trying to identify the community structure using the community_spinglass function from the igraph API. The weighted graph data represents the connections between individuals and the corresponding weights indicate the strength of these connections. You are looking to use the community_spinglass function to analyze and identify the communities within the network, which can help in understanding the potential pathways for virus transmission within the community.
### Response:
from igraph import Graph

# Create a graph with edge weights
edges = [(0, 1), (1, 2), (2, 3), (0, 3), (3, 4), (4, 5), (5, 3)]
weights = [1, 2, 1, 3, 1, 2, 1]
g = Graph(edges=edges)
g.es['weight'] = weights

# Find the community structure
communities = g.community_spinglass(weights='weight', spins=4)

# Print the membership of nodes in communities
print(communities.membership)
# Print the modularity score
print(communities.modularity)
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