<｜begin▁of▁sentence｜>### Instruction:
Imagine you're deployed to assist in organizing a relief network following a severe natural disaster. This network consists of three groups involved in the operations: solo experts (Group A with 1 member), duo response teams (Group B with 2 members), and trio field units (Group C with 3 members). You're tasked with setting up a communication framework where each group is connected with every other member from the other groups, ensuring comprehensive coordination across the whole relief effort.

To accomplish this with the help of digital tools, you plan to design a communication graph using the NetworkX library that represents this complete multipartite structure. For logistical clarity, you aim to identify which group each individual or team belongs to within this network. Furthermore, your immediate concern is to determine the specific contacts for the initial duo team in Group B – meaning, you need a list of the solo expert and trio field units they must establish communication with promptly.

Could you restructure this query into our working context, keeping the technical aspects in check, while considering how digital mapping of our network can facilitate an effective and streamlined relief operation?
### Response:
import networkx as nx

# Create a complete tripartite graph with subsets of sizes 1, 2, and 3
G = nx.complete_multipartite_graph(1, 2, 3)

# Print the subsets to which each node belongs
subsets = [G.nodes[u]['subset'] for u in G]
print('Node subsets:', subsets)

# Print edges connected to node 0
edges_node_0 = list(G.edges(0))
print('Edges connected to node 0:', edges_node_0)

# Print edges connected to node 2
edges_node_2 = list(G.edges(2))
print('Edges connected to node 2:', edges_node_2)
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