<｜begin▁of▁sentence｜>### Instruction:
In the spirited world of corporate communications, we often draw parallels between the intricate networks of human interactions and the complex connections within theoretical graphs. Imagine, for a moment, two distinct yet seemingly similar networks within the corporate spectrum. The first, let's call it Network Alpha, represents a trio of entities—let's say departments within an organization—which are interconnected: Department 1 liaises with Department 2, Department 2 with Department 3, and Department 3 in turn completes the loop by linking back to Department 1. This cycle of collaboration is the hallmark of Network Alpha, and to symbolize the seamless synergy between these departments, we have color-coded this relationship 'red' for its strength and vitality.

In a similar vein, we have Network Beta, an alliance within a different organization or perhaps a competing conglomerate. This network also boasts a triumvirate of departments—'A', 'B', and 'C'—with a communication flow reminiscent of Network Alpha: Department 'A' to 'B', 'B' to 'C', and 'C' back to 'A'. This mirror image of interaction is likewise tinted in 'red', signaling an analogous dynamic of interdepartmental cohesion.

In the grand scheme of corporate networking and public relations, one might ponder: Are Network Alpha and Network Beta fundamentally identical in their structure and interrelations, should we take into account the 'red' color that signifies the nature of their connections? This forms the crux of our professional curiosity, as we delve into the nuances of networking models and their implications in the broader context of organizational communication strategies. Could you, drawing from your expertise, ascertain the equivalence of these two networks—bearing in mind the distinct 'hue' which garners them their characteristic vibrance?
### Response:
import networkx as nx
from networkx.algorithms.isomorphism import is_isomorphic, categorical_node_match

# Define the first graph
G1 = nx.Graph()
G1.add_nodes_from([1, 2, 3], color='red')
G1.add_edges_from([(1, 2), (2, 3), (3, 1)])

# Define the second graph
G2 = nx.Graph()
G2.add_nodes_from(['a', 'b', 'c'], color='red')
G2.add_edges_from([('a', 'b'), ('b', 'c'), ('c', 'a')])

# Define node_match to consider the node attribute 'color'
nm = categorical_node_match('color', 'red')

# Check if the two graphs are isomorphic considering the node color
iso = is_isomorphic(G1, G2, node_match=nm)

print('Are G1 and G2 isomorphic considering node color?', iso)
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