<｜begin▁of▁sentence｜>### Instruction:
Inside the intricate web of connections represented by our "social network" case – a perfectly interconnected group of five individuals – we're tasked with unraveling a particular mystery. Imagine this network as a series of bonds, akin to a complete graph we've drafted on our virtual investigation board, meticulously constructed using NetworkX as our detective's tool of choice.

In our investigative scenario, let's consider each link a potential lead, and every triangle formed by these links a subunit of our network, possibly holding clues to the overarching puzzle. Our objective: to ascertain the count of triangles that a specific individual (node) is entangled with, a key insight that might shed light on their influence or connections within this group.

Moreover, for a comprehensive understanding of the entire network dynamics, we're curious about the total number of such triangles that each member of this network forms part of. This information could prove instrumental in piecing together the bigger picture of our social enigma.

For the sake of our investigation, let's presume that this network's data and relationships are encapsulated within a file dubbed "social_connections.gml", which holds the key to untangling the intricate web of connections and identifying the crucial triangles that each individual forms a part of.
### Response:
import networkx as nx

# Create a complete graph with 5 nodes
G = nx.complete_graph(5)

# Compute the number of triangles for node 0
triangles_node_0 = nx.triangles(G, 0)
print('Number of triangles involving node 0:', triangles_node_0)

# Compute the number of triangles for all nodes
all_triangles = nx.triangles(G)
print('Number of triangles for all nodes:', all_triangles)

# Compute the number of triangles for nodes 0 and 1
triangles_0_and_1 = nx.triangles(G, [0, 1])
print('Number of triangles involving nodes 0 and 1:', triangles_0_and_1)
<|EOT|>
