<｜begin▁of▁sentence｜>### Instruction:
As a knowledgeable assistant proficient in networkx, imagine working as a data scientist for a tech company focused on social media platforms. In this scenario, you are tasked with analyzing the network connections within the company's user base. You are provided with a list of edges representing friendships between users: [(0, 1), (0, 2), (2, 3), (4, 5), (5, 6), (7, 8), (8, 9)]. Your goal is to leverage the `size_histogram` function in NetworkX to calculate the histogram of cluster sizes based on the specified bin width. By interpreting these cluster sizes, you aim to gain insights into the size distribution of user communities within the platform.
### Response:
from igraph import Graph

# Example usage
# Create a graph with some clusters
edges = [(0, 1), (0, 2), (2, 3), (4, 5), (5, 6), (7, 8), (8, 9)]
g = Graph(edges=edges)

# Perform clustering analysis on the graph
clusters = g.clusters()

# Get the histogram of cluster sizes using the specified bin width
size_hist = clusters.size_histogram(bin_width=1)

# Print the cluster size histogram
counts = size_hist.counts
print('Cluster size histogram:', counts)
<|EOT|>
