<｜begin▁of▁sentence｜>### Instruction:
As a fashion blogger, you've been curating content for your latest blog post, focusing on the interconnectedness of fashion trends and styles. While researching, you stumbled upon a fascinating analogy between network connectivity and fashion influences within a community. You're intrigued to explore this concept further.

You recently received a dataset, 'sample.gml', containing information about the connections between different fashion influencers and their impact on trends. Now, you're eager to analyze this network to determine its level of connectivity.

To achieve this, you're considering leveraging the `is_connected` function from the `igraph` library, known for its robust network analysis capabilities. Your goal is to ascertain whether the fashion influencer network exhibits strong or weak connectivity. This insight will add a unique perspective to your upcoming blog post, highlighting the intricate web of relationships shaping fashion trends.
### Response:
import igraph as ig

# Read the graph from file
graph = ig.read('sample.gml')

# Check if the graph is strongly connected
is_strongly_connected = graph.is_connected(mode=ig.STRONG)

# Check if the graph is weakly connected
is_weakly_connected = graph.is_connected(mode=ig.WEAK)

print('Strongly connected:', is_strongly_connected)
print('Weakly connected:', is_weakly_connected)
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