<｜begin▁of▁sentence｜>### Instruction:
As a content creator, you're currently developing an engaging infographic to visualize a complex network of influencers who collaborate and influence each other within your niche. To deepen your understanding and inform your design, you've mapped their collaboration history onto a directed, weighted graph where the nodes represent influencers and the edges represent collaborations. The weight of an edge signifies the value or depth of the collaboration (0, 1, 5 could translate to influencer 0 collaborating with influencer 1 with a value of 5). 

To complete your infographic, you're interested in knowing about the shortest path lengths between all pairs of influencers, considering the value of their past collaborations. The question now is: How could you use the Floyd-Warshall algorithm, a concept from NetworkX, to calculate these path lengths in this influencers network?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add weighted edges
G.add_weighted_edges_from([(0, 1, 5), (1, 2, 2), (2, 3, -3), (1, 3, 10), (3, 2, 8)])

# Run Floyd-Warshall algorithm to find all-pairs shortest path lengths
fw = nx.floyd_warshall(G, weight='weight')

# Convert the results to a dictionary
results = {a: dict(b) for a, b in fw.items()}

# Display the shortest path lengths between nodes
print(results)
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