<｜begin▁of▁sentence｜>### Instruction:
As a Human Resources Manager, I need to ensure that teams within our organization are built on diverse yet compatible sets of skills and connections. We have two project teams - Team Alpha and Team Bravo - both structured in unique ways, similar to networks where team members are nodes and their working relationships are edges between these nodes. Team Alpha's connections are delineated as partnerships between members 1 & 2, 2 & 3, and so forth, listed as [(1,2), (2,3), (2,4), (3,5), (4,6), (5,7), (6,7)], while Team Bravo's are identified between members 1 & 3, 2 & 3, etc., noted as [(1,3), (2,3), (1,6), (1,5), (4,6)].

To promote cross-functional collaboration and understand better how these teams might work together or where shared strengths and weaknesses may lie, I am considering using an advanced analytical approach called the Weisfeiler-Lehman subgraph hashing. This technique would allow us to see if there are members within Team Alpha and Team Bravo who share similar patterns in their teamwork and communication styles, which are indicative of their local network topologies within the teams' structures. 

Could we leverage this method to identify any resemblances in the team dynamics by comparing the underlying subgraph structures? Identifying such patterns could be invaluable for aligning teams on collaborative projects or in strategic planning for training needs assessment.
### Response:
import networkx as nx

# Create two graphs
G1 = nx.Graph()
G1.add_edges_from([(1,2), (2,3), (2,4), (3,5), (4,6), (5,7), (6,7)])
G2 = nx.Graph()
G2.add_edges_from([(1,3), (2,3), (1,6), (1,5), (4,6)])

# Compute WL subgraph hashes
wl_hashes_g1 = nx.weisfeiler_lehman_subgraph_hashes(G1, iterations=3, digest_size=8)
wl_hashes_g2 = nx.weisfeiler_lehman_subgraph_hashes(G2, iterations=3, digest_size=8)

# Output the hashes for a specific node from each graph
print('Hashes for node 1 in G1:', wl_hashes_g1[1])
print('Hashes for node 5 in G2:', wl_hashes_g2[5])
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