<｜begin▁of▁sentence｜>### Instruction:
As a proficient color consultant, you usually advise clients on color schemes, paint selection, and interior color coordination. Now, imagine a scenario where a large enterprise has decided to rely on your expert opinion for their office color scheme based on the interaction levels between different departments. They have four key departments: 'A', 'B', 'C', and 'D', and they have provided you with a dataset that contains the weighted interactions between these departments. The weightage stands for the level of interaction and collaboration between any two departments.

Your task is to create a reliable and easy-to-interpret visual representation of these interactions. You have decided to present this complicated data in the form of a network graph to highlight the strongest and weakest interactions. To keep this graph optimized in terms of memory, you plan to convert the department names to integers.

You remember the GraphBuilder utility from the graspologic.pipeline that you have previously worked with for creating such network graphs. So, how would you go about adding these weighted interactions or edges between departments to the GraphBuilder? And, what will be your strategy to build the final networkx graph using this tool?
### Response:
import networkx as nx
from graspologic.pipeline import GraphBuilder

# Initialize GraphBuilder (assume we are creating an undirected graph)
grb = GraphBuilder(directed=False)

# Add weighted edges between nodes
# Note: Nodes can be any hashable type
edges = [('A', 'B', 5.2), ('B', 'C', 3.1), ('C', 'D', 7.4), ('D', 'A', 2.6)]

# Using add_edge method to add edges to the GraphBuilder
gb.add_edge('A', 'B', weight=5.2)
gb.add_edge('B', 'C', weight=3.1)
gb.add_edge('C', 'D', weight=7.4)
gb.add_edge('D', 'A', weight=2.6)

# Building the graph, getting node mapping, and the list of unique nodes
graph, id_mapping, unique_nodes = grb.build()

# Now we can work with the generated graph using networkx functions
print(nx.info(graph))
<|EOT|>
