<｜begin▁of▁sentence｜>### Instruction:
Imagine you are assisting in a research project in a laboratory, where understanding complex interaction networks is vital for breakthrough discoveries. You've been tasked to model a specifically structured interaction network using NetworkX — a complete multipartite graph that reflects a hypothetical scenario with three groups (or subsets) of different elements. The first group should consist of a single element, the second group should contain two elements, and the third should have three elements.

In one instance of the experiment, the elements are identified by simple integers, representing different entities or conditions. In a separate instance, the elements are represented by more complex iterables, which might correspond to various concentrations or permutations of a set of compounds.

For both scenarios, it is crucial to not only create the network but also to catalog the groupings of the elements and to specifically examine the connections emanating from the "first" element of each group within this multipartite graph — akin to highlighting specific pathways or interactions in the laboratory model being studied.

To fully understand the interaction dynamics of the system, please reframe the task by addressing it in terms of practical applications in the lab context while maintaining the objective of modeling the interaction network with the outlined specifications using the NetworkX library.
### Response:
import networkx as nx

# Creating a complete multipartite graph with subset sizes 1, 2, and 3
G = nx.complete_multipartite_graph(1, 2, 3)

# Print nodes and their subsets
print([(u, G.nodes[u]['subset']) for u in G])

# Print edges connected to node 0
print(list(G.edges(0)))

# Creating a complete multipartite graph using iterables
H = nx.complete_multipartite_graph('a', 'bc', 'def')

# Print nodes and their subsets for graph H
print([(u, H.nodes[u]['subset']) for u in sorted(H)])
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