<｜begin▁of▁sentence｜>### Instruction:
As a political scientist, you're tasked with examining the intricate dynamics of political systems and institutions, aiming to shed light on various phenomena and forecast future trends. Imagine you're analyzing a network of political alliances within a nation's legislature. By utilizing the NetworkX library, you aim to determine the number of weakly connected components within this network, providing insights into the cohesion and potential cooperation among different factions or parties. To accomplish this, you would employ NetworkX's `weakly_connected_components` function to discern these clusters within the political landscape, enabling a nuanced understanding of the interplay between various political entities.
### Response:
from igraph import Graph

# Load graph from a file
g = Graph.Read('network.gml')

# Calculate the weak connected components
components = g.connected_components(mode='weak')

# Print the number of weakly connected components
print('Number of weakly connected components:', len(components))
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