<｜begin▁of▁sentence｜>### Instruction:
As a Meeting, Convention, and Event Planner, you're tasked with orchestrating gatherings that fulfill specific objectives while ensuring seamless execution. Imagine you're organizing a professional networking event for a group of six professionals from diverse fields. To enhance networking opportunities, you're keen to assess the compatibility between attendees based on their interests and expertise.

Suppose you have data indicating the relationships between these professionals, represented as an undirected Erdős-Rényi random graph with six vertices and ten edges, reflecting potential connections among attendees. Leveraging the GraphBase.similarity_jaccard function in igraph, you aim to compute the Jaccard similarity coefficient for all pairs of attendees, providing insights into their shared interests and potential synergies.

Your objective is to generate a similarity matrix that quantifies the level of similarity between each pair of attendees, aiding in strategic seating arrangements and facilitating meaningful interactions during the event. This matrix will serve as a valuable tool in fostering connections and fostering a vibrant networking atmosphere conducive to productive exchanges among participants.
### Response:
import igraph as ig

# Create a graph
graph = ig.Graph.Erdos_Renyi(n=6, m=10, directed=False)

# Calculate Jaccard similarity coefficient for all pairs of vertices
similarity_matrix = graph.similarity_jaccard()

# Print the similarity matrix
print(similarity_matrix)
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