<｜begin▁of▁sentence｜>### Instruction:
As a concierge, a guest has reached out for advice on the optimal number of organized activities for a group of guests at the resort. To assist them in determining the best approach, we'll use the resort layout as a simple undirected graph with the following paths: activity A to B, activity A to C, activity B to C, and activity B to D. We can refer to the optimal_count attribute of the VertexDendrogram in the NetworkX library to identify the optimal number of clusters for grouping these activities using edge betweenness.
### Response:
from igraph import Graph

# Construct a graph
edges = [(0, 1), (0, 2), (1, 2), (1, 3)]
g = Graph(edges)

# Get the dendrogram object from community detection
vd = g.community_edge_betweenness(directed=False).as_clustering()

# Find the optimal count for the dendrogram
optimal_count = vd.optimal_count
print('Optimal count:', optimal_count)
<|EOT|>
