<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting a visual narrative for a digital illustration that depicts an ever-growing community of artists. In this artistic network, you start with just one artist, and over time, more artists join the scene. Each new artist chooses to collaborate with an existing one, picking their partners based on how connected and well-known they already are—the more connections an artist has, the more likely they'll attract newcomers.

We'd like to bring this dynamic network to life in an interactive digital piece where each artist is represented as a node. For this, we require a detailed JSON map that showcases the intricacies of these collaborations. Each artist (node) needs a unique 'id' and a 'label' that could include their name or art style. The collaborations (edges) need to define their 'source' artist, their 'target' artist, and a 'label', perhaps depicting the type of collaboration or medium used.

Could you transform the structure of this creative network into such a JSON document, granting us a behind-the-scenes look at the community's formation, and ensuring that the document can be used to guide viewers through the network’s evolution in our visual storytelling piece? If the details of this network are contained in a file, let's refer to it as "creative_artists_network.gml" and make sure to include any relevant data that will contribute to the depth and accuracy of the scenario.
### Response:
import networkx as nx

# Function to generate a GN graph
def generate_gn_graph(n, kernel=None, seed=None):
    # Create a GN graph
    G = nx.gn_graph(n, kernel=kernel, seed=seed)
    # Convert the graph to the undirected version if needed
    G_undirected = G.to_undirected()
    
    # Generate the JSON representation
    json_graph = {'graph nodes': [], 'graph edges': []}
    
    for node in G_undirected.nodes():
        json_graph['graph nodes'].append({'id': node, 'label': f'Node {node}'})
    
    for edge in G_undirected.edges():
        json_graph['graph edges'].append({'source': edge[0], 'target': edge[1], 'label': f'Edge from {edge[0]} to {edge[1]}'})
    
    return json_graph

# Example: Generate a GN graph with 5 nodes
graph_json = generate_gn_graph(5)
print(graph_json)
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