<｜begin▁of▁sentence｜>### Instruction:
Imagine we're analyzing the intricate patterns of interaction within a historical society, depicted through a graph akin to a MultiGraph in NetworkX. In our scenario, the graph reflects various social connections within a community, where node 1 represents a notable figure who has both a connection to themselves—a self-reference perhaps indicating self-reliance or introspection—and a connection to node 2, symbolizing an interaction with another individual.

To truly understand the social dynamics and the subtleties of self-reference in this community, we must catalogue all instances of such self-referential connections. Could you demonstrate how one might meticulously catalogue each self-referential link within this graph, akin to noting the specific characteristics of individual garments in a historical wardrobe? Additionally, for a comprehensive understanding, please describe the process by which one could extract these self-referential connections complete with any associated details and identifiers, just as a costume historian would gather detailed information about the fabric, stitching, and patterns of period costumes.
### Response:
import networkx as nx

# Create a MultiGraph
G = nx.MultiGraph()

# Add selfloop edge and another edge
selfloop_ekey = G.add_edge(1, 1)
G.add_edge(1, 2)

# List all selfloop edges
selfloop_edges = list(nx.selfloop_edges(G))

# List all selfloop edges with data
selfloop_edges_data = list(nx.selfloop_edges(G, data=True))

# List all selfloop edges with keys
selfloop_edges_keys = list(nx.selfloop_edges(G, keys=True))

# List all selfloop edges with keys and data
selfloop_edges_keys_data = list(nx.selfloop_edges(G, keys=True, data=True))

print('Selfloop edges:', selfloop_edges)
print('Selfloop edges with data:', selfloop_edges_data)
print('Selfloop edges with keys:', selfloop_edges_keys)
print('Selfloop edges with keys and data:', selfloop_edges_keys_data)
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