<｜begin▁of▁sentence｜>### Instruction:
Imagine you're part of a healthcare network's IT department, working closely with medical professionals who communicate primarily through emails. In this network, each physician and administrative staff is a node, and the emails sent from one person to another form directed edges between these nodes. The frequency of emails exchanged between any two individuals is crucial, as it's used to determine the intensity of their communication, represented as the weight of each edge in this digital communication map.

Your task involves constructing a directed graph to accurately model this email communication network. This graph will need to incorporate both the directionality of the emails (who sent an email to whom) and the volume of communication (how often emails are exchanged between each pair), with the latter serving as the edge weights. 

The ultimate goal is to utilize this graph to identify the most efficient pathway for information flow between two specific members of the healthcare network. This involves finding the shortest path in terms of the weighted edges, ensuring that urgent information can be relayed in the most direct and least time-consuming manner possible. 

To achieve this, you're considering the use of GraphBuilder, a tool that can streamline the construction of such a directed and weighted graph. Once this graph is established, it must be compatible with the networkx library, a Python package that you plan to utilize for analyzing complex networks and finding the shortest paths among nodes.

How can you leverage GraphBuilder to create this detailed, directed graph from the existing email logs? And how can this process be seamlessly integrated with networkx functionalities, specifically for executing algorithms that pinpoint the shortest communication path between any two given individuals within the healthcare network's email system?
### 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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