<｜begin▁of▁sentence｜>### Instruction:
Imagine we are developing a therapeutic exercise for a client focused on understanding the concept of self-referential structures as a part of a cognitive rehabilitation program. We are utilizing a graphical representation to illustrate the idea of self-perception, where nodes are akin to individuals, and edges symbolize their relationships or interactions. Correspondingly, a self-loop could be seen as a person's self-talk or introspection.

In our exercise setup, we have a data-encoded diagram that is a simple visualization of a social network that includes both self-referential interactions and interactions with others. The data provided, let's nome it "social_network_data", includes pairs such as ('1', '1') indicating a self-loop, and ('1', '2'), demonstrating a relationship between two different individuals.

Given this context, how can we pinpoint the individuals within our diagram who engage in self-reflection or self-dialogue, symbolized by the presence of self-loops in the network diagram structured by NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
# Add edges including a self-loop
G.add_edge(1, 1)
G.add_edge(1, 2)
# Find nodes with self-loops
nodes_with_loops = list(nx.nodes_with_selfloops(G))
print(nodes_with_loops)
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