<｜begin▁of▁sentence｜>### Instruction:
Imagine you're analyzing a company's decision-making process, which can be visualized as a flowchart where each step leads to one or more subsequent steps, but never circles back to a previous one. Your flowchart is currently represented by the following sequence of steps: [(Step 1, Step 2), (Step 1, Step 3), (Step 2, Step 4), (Step 3, Step 4)]. 

For a more refined analysis, you'd like to map out a "branching" model that simplifies the decision tree into a hierarchy, maintaining the directional nature of decisions but in a structure that makes it easier to visualize and understand individual paths and their relationships. Using NetworkX, you aim to transform your current flowchart into this branching representation, but you want to ensure that you can still pinpoint exactly which decisions (or nodes) in the new, simplified model correspond to those on your original detailed flowchart. Could you describe how you would proceed with such a conversion within the scope of your behavioural economics study?
### Response:
import networkx as nx
from collections import defaultdict

# Create a directed acyclic graph (DAG)
G = nx.DiGraph()
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4)])

# Convert DAG to branching
B = nx.dag_to_branching(G)

# Print the edges of the branching
print(list(B.edges()))

# Map the nodes in the branching back to the original nodes in G
sources = defaultdict(set)
for v, source in B.nodes(data='source'):
    sources[source].add(v)

# Print the mapping
print(dict(sources))
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