<｜begin▁of▁sentence｜>### Instruction:
Imagine you're analyzing the interconnected network of various online platforms where 'A', 'B', 'C', and 'D' represent these platforms, and they share partnerships with varying degrees of collaboration, indicated by the weights 4, 2, 1, and 3 respectively. These partnerships are captured in our latest digital marketing data report, forming partnerships like 'A-B' with a weight of 4, 'B-C' with a weight of 2, and so on. 

As a digital marketing specialist looking to fine-tune our online campaign's reach, how would you compute the average influence score of the connected network for each platform, considering the weight of the partnerships? This average influence score will help us understand which platform's network could offer the most value for our marketing efforts based on the strength of its connections. 

Please take the weighted interactions outlined in the data report into account, as this will provide insight into the significance of each platform's position within the broader network.
### Response:
import networkx as nx

# Create a weighted undirected graph
G = nx.Graph()
G.add_edge('A', 'B', weight=4)
G.add_edge('B', 'C', weight=2)
G.add_edge('C', 'D', weight=1)
G.add_edge('D', 'A', weight=3)

# Calculate the average neighbor degree for the graph
avg_neighbor_degree = nx.average_neighbor_degree(G, weight='weight')

print(avg_neighbor_degree)
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