<｜begin▁of▁sentence｜>### Instruction:
Imagine you're managing the social media strategy for a new tech conference, and you want to create a network graph to visually represent the relationships between different tech influencers attending the event. You have identified three key influencers (Node1, Node2, and Node3) and you've noted the connections between them through their collaborations (Edge1 between Node1 and Node2, and Edge2 between Node2 and Node3). 

To further analyze the structure of this network and optimize your social media engagement strategy, you'd like to calculate the s-metric, a measure of the graph's structural organization, which can inform you about the potential virality and connectivity of the content within this influencer network. Using the specifics of your network graph (where nodes are influencers and edges are their collaborative connections), how would you go about computing the s-metric using NetworkX?

Keep in mind, the data for your influencers is as follows:
Graph nodes: [{'id': 1, 'label': 'Node1'}, {'id': 2, 'label': 'Node2'}, {'id': 3, 'label': 'Node3'}]
Graph edges: [{'source': 1, 'target': 2, 'label': 'Edge1'}, {'source': 2, 'target': 3, 'label': 'Edge2'}]

This calculation will empower you with a deeper understanding of the conference's social network, thereby enabling you to tailor your content and engage with your audience in the most impactful way.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add nodes and edges to the graph
G.add_nodes_from([(1, {'label': 'Node1'}), (2, {'label': 'Node2'}), (3, {'label': 'Node3'})])
G.add_edges_from([(1, 2, {'label': 'Edge1'}), (2, 3, {'label': 'Edge2'})])

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