<｜begin▁of▁sentence｜>### Instruction:
A company has an email communication network where nodes represent employees and directed edges indicate who sent an email to whom. The weight of an edge represents the frequency of communication. The company wants to create a directed graph from this network, find the shortest communication path between two specific employees, and ensure the final graph can work with functions from the networkx library. How can GraphBuilder be used to build this graph, accounting for the direction and weight of interactions, and integrate with networkx to find the shortest path?
### Response:
import networkx as nx
from graspologic.pipeline import GraphBuilder

# Initialize the GraphBuilder for a directed graph
graph_builder = GraphBuilder(directed=True)

# Add edges with weights
graph_builder.add_edge('user1', 'user2', weight=2.5)
graph_builder.add_edge('user2', 'user3', weight=1.0)
graph_builder.add_edge('user3', 'user1', weight=3.0)

# Build the graph
graph, node_id_map, original_node_list = graph_builder.build()

# Convert node IDs back to original labels
nx.set_node_attributes(graph, {node_id_map[original]: original for original in original_node_list}, 'original_label')

# Now work with the graph using networkx functions
# For example, find shortest path
shortest_path = nx.shortest_path(graph, source=node_id_map['user1'], target=node_id_map['user3'], weight='weight')

# Translate back to original node labels
shortest_path_original_labels = [original_node_list[node] for node in shortest_path]
print('Shortest path: ', shortest_path_original_labels)
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