<｜begin▁of▁sentence｜>### Instruction:
Imagine you are the bookkeeper for a corporation that manages a network of partnerships and associations between various departments (nodes) and different types of collaborations (edges). You have a ledger that details the relations—a graph, if you will. The departments are sorted into two categories for simplicity: 'Red' for core departments ('A', 'B', 'C', 'D') and 'Blue' for auxiliary ones ('E', 'F'). Inter-departmental collaborations are recorded based on their strength—'Strong' or 'Weak'.

Now, you need to simplify this ledger into a condensed report that still captures the essential structure of the network. You're interested in the SNAP (Structural Node Aggregation by Partitioning) methodology, which is akin to creating a summarized balance sheet from detailed financial data.

The detailed report, which we'll treat as an equivalent to a GML file in this metaphorical scenario, presents the following data: departments 'A' to 'D' collaborate with 'E' and 'F'. The 'A' and 'E' partnership is noted as having 'Strong' ties, just as the 'B' and 'F' connection. Conversely, 'C' and 'E', as well as 'D' and 'F', have 'Weak' associations.

Given this information, how would you leverage the SNAP technique inherent in NetworkX to generate a more concise depiction of our departmental network chart, one that would fit into a streamlined quarterly financial summary? Keep in mind that the essence and nuances of the original complex inter-departmental graph must be preserved in the reduced version to ensure accurate and strategic planning for potential future investments or restructuring.
### Response:
import networkx as nx
from networkx.algorithms.summarization import snap_aggregation

# Create a graph
G = nx.Graph()

# Define node attributes
g_nodes = {
    "A": {"color": "Red"},
    "B": {"color": "Red"},
    "C": {"color": "Red"},
    "D": {"color": "Red"},
    "E": {"color": "Blue"},
    "F": {"color": "Blue"},
}

# Define edges with types
g_edges = [
    ("A", "E", "Strong"),
    ("B", "F", "Strong"),
    ("C", "E", "Weak"),
    ("D", "F", "Weak"),
]

# Add nodes and edges to the graph
for node, attributes in g_nodes.items():
    G.add_node(node, **attributes)
for source, target, edge_type in g_edges:
    G.add_edge(source, target, type=edge_type)

# Define attributes to be considered
node_attributes = ('color',)
edge_attributes = ('type',)

# Perform SNAP aggregation
summary_graph = snap_aggregation(G, node_attributes=node_attributes, edge_attributes=edge_attributes)

# Output the summary graph
print(summary_graph.nodes(data=True))
print(summary_graph.edges(data=True))
<|EOT|>
