<｜begin▁of▁sentence｜>### Instruction:
Imagine you're designing a new recreational group activity aimed at enhancing cooperation among participants with diverse abilities. In preparation, you've conceptualized the activity as a network where each participant is a node and each potential interaction is an edge. So far, the potential interactions you've mapped out are as follows: interactions between participant 1 and both participants 2 and 3, between participant 2 and both participants 3 and 4, and an interaction between participants 3 and 4. 

In order to ensure the activity runs smoothly and fosters the ideal level of engagement, you want to assess the connectedness of the group using a measure from network science. Could you please illustrate how one would apply the 's-metric' evaluation, a technique used in network analysis to measure this aspect of the activity's social interaction structure, utilizing the NetworkX library? Keep in mind, the more connected the network, the higher the s-metric score, suggesting a highly collaborative and potentially more enjoyable group activity.

Remember, the interactions you are considering in this scenario are represented as edges in a graph, which are (1, 2), (1, 3), (2, 3), (2, 4), and (3, 4). How would one calculate the s-metric of this interaction graph with NetworkX to evaluate the potential for connectivity in the planned activity?
### Response:
import networkx as nx

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

# Calculate the s-metric of the graph
s_metric_value = nx.s_metric(G, normalized=False)

print('S-metric of the graph:', s_metric_value)
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