<｜begin▁of▁sentence｜>### Instruction:
Imagine you're looking into the interconnections of two distinct types of entities within your dental practice management software: let's call them 'Dentists' (nodes 'a', 'b') and 'Services' (nodes 1, 2). The relations between them are appointments where a certain dentist is linked to a particular service, akin to a dentist providing different types of dental treatments.

Current appointment records are as follows:

- Dentist 'a' provides Service 1.
- Dentist 'b' provides Service 1.
- Dentist 'a' provides Service 2.
- Dentist 'b' provides Service 2.

In this scenario, Services are like shared patients between dentists. You now want to construct a new perspective or a simplified appointment history graph where you can visualize direct relationships between the dentists themselves based on their shared patients. In this new graph, there will be an edge connecting two dentists for each patient they have in common.

How would you reorganize your appointment records to reflect the shared service frequency directly between 'Dentist a' and 'Dentist b', acknowledging that each shared patient event would translate to a 'connection' in this newly projected multigraph of dentist-to-dentist interactions? Remember, this projection is conceptual and doesn't need to be displayed on your software—just outlined as a framework.
### Response:
from networkx.algorithms import bipartite
B = nx.Graph()
B.add_edges_from([('a', 1), ('b', 1), ('a', 2), ('b', 2)])
G = bipartite.projected_graph(B, ['a', 'b'], multigraph=True)
print([sorted((u, v)) for u, v in G.edges()])
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