<｜begin▁of▁sentence｜>### Instruction:
As a Social Media Manager, you may encounter the need to visually represent networks or connections within your social media content. For instance, you might want to show the interrelationships between different influencers, followers, or content categories. In this scenario, using network analysis tools such as Python's NetworkX library can be valuable.

Let's consider a situation where you want to visualize a network graph of 100 nodes representing influencers on a social media platform. To effectively communicate this network, you might want to create a visually appealing PDF file for sharing in reports or presentations. Utilizing the CairoPlot library in igraph, you can create a PDF file named 'output_graph.pdf' with a size of 600x400 and a white background, containing the visualization of the influencer network graph generated using the Erdős-Rényi model with a 0.1 probability of edge creation. This visual representation can help convey the relationships and connectivity within the influencer network to stakeholders and colleagues.
### Response:
import igraph as ig
import cairo

# Assume we have some graph data
g = ig.Graph.Erdos_Renyi(n=100, p=0.1)

# File to write to
target_file = 'output_graph.pdf'

# Initialize plot with specified bounding box (width x height) and background color
plot = ig.plot.CairoPlot(target=target_file, bbox=(600, 400), background='white')

# Assign the graph to be plotted
plot.add(g)

# Save the plot to the file
plot.show()
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