<｜begin▁of▁sentence｜>### Instruction:
In the context of preparing evidence for an upcoming mediation session, consider that we are constructing a digital model to simulate the complex network of communications between involved parties. To accurately reflect the randomness of interactions, we intend to create a virtual representation of this network. We plan to use the NetworkX library to generate a theoretical model with precisely ten individuals, represented as nodes, and the likelihood of any two individuals having communicated at a 50% chance. To maintain consistency during simulation trials, we're using a predetermined numerical value of 42, ensuring that the results are replicable upon subsequent analysis.

I'd like to request the generation of a structured digital document in JSON format, encapsulating this hypothetical communication network. How will the interactions (edges) between the individuals (nodes) be detailed within this JSON document to ensure clarity when presented as part of the mediation documentation? Please ensure that this information is seamlessly incorporated into the scenario, without actually implementing or solving the model generation at this stage. If this network model is to be saved as a GML file, kindly integrate this detail into your scenario, adjusting the context accordingly to involve the file, provisionally named "mediation_communication_network.gml".
### Response:
import networkx as nx

# Parameters for generating the graph
n = 10  # number of nodes
p = 0.5  # probability of edge creation
seed = 42  # seed for random number generator (optional)

# Generate a G_{n,p} random graph
gnp_graph = nx.fast_gnp_random_graph(n, p, seed, directed=False)

# Now that we have gnp_graph, let's create a JSON object representing its nodes and edges.
graph_data = {'graph nodes': [], 'graph edges': []}
for node in gnp_graph.nodes():
    graph_data['graph nodes'].append({'id': node, 'label': f'Node {node}'})

for edge in gnp_graph.edges():
    graph_data['graph edges'].append({'source': edge[0], 'target': edge[1], 'label': f'{edge[0]}-{edge[1]}'})

# Displaying the graph data in JSON format
print(graph_data)
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