<｜begin▁of▁sentence｜>### Instruction:
Imagine we are examining a network of individuals within a community support group, where each connection between members signifies a shared experience or support interaction. The pairs of members and their connections are represented as follows: member 1 is connected to members 2 and 3, member 2 is also connected to member 3, member 3 has a connection with member 4, member 4 with member 5, and member 5 has come full circle by connecting back to member 1.

In helping each individual understand their role within the support system, we are interested in identifying how critical each person's involvement is in terms of facilitating communication within the group. This concept, in the realm of network analysis, is known as "load centrality". Using this measure, we aim to figure out how significant each member is as a conduit for information flow among the others. Employing the NetworkX tool, we wish to calculate the load centrality for each member. This will enable us to provide informed guidance on where targeted support or interventions might be needed to enhance the resilience and connectedness of the community network. 

In providing your expertise, please make sure that when you calculate these metrics, you consider each individual as a node in the network and their interactions as edges within it. The edge set of the network is given as: [(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (5, 1)]. How can we compute the load centrality for each node to better understand the underlying dynamics of this support network?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (5, 1)])

# Compute load centrality
load_c = nx.load_centrality(G, normalized=True, weight=None, cutoff=None)

# Print load centrality of each node
for node, centrality in load_c.items():
    print(f'Node {node}: Load Centrality = {centrality}')
<|EOT|>
