<｜begin▁of▁sentence｜>### Instruction:
As a Meeting, Convention, and Event Planner, imagine you have been hired to organize a large conference for a professional network of data scientists. As part of your planning process, you have been provided with a graph representing the connections between the members of this network, and a list indicating the community membership of each data scientist based on their research interests.

To ensure that the conference program fosters interaction within and between these communities, you are looking to assess the modularity of the graph with respect to the given community memberships. Using a tool like NetworkX, you can leverage the modularity function to calculate the degree to which the network's structure aligns with the community divisions, helping you to plan targeted networking sessions and discussions that maximize meaningful connections and collaboration among the data scientist attendees.
### Response:
from igraph import Graph

# Load a graph from a file
graph = Graph.Read_GML('graph_data.gml')

# Assume that we have a membership vector denoting the community membership of each vertex
membership = [0, 0, 1, 1, 1, 2, 2, 2]
# You can define weights if your graph is weighted
weights = graph.es['weight'] if graph.is_weighted() else None

# Calculate the modularity given the membership and optionally weights
modularity_score = graph.modularity(membership, weights=weights)

# Print the modularity score
print('Modularity:', modularity_score)
<|EOT|>
