<｜begin▁of▁sentence｜>### Instruction:
As an IT Support Specialist, let's put this in a scenario where you're troubleshooting a network issue for a company's IT infrastructure. Imagine you're tasked with analyzing the connectivity and interactions between various devices in the network, represented as vertices in a graph, with the connections between them being weighted edges indicating the strength of their relationship.

To effectively assess the similarity between these devices based on their interactions, you might consider employing a method called the "inverse log-weighted similarity coefficient." This coefficient quantifies the similarity between vertices in the graph, taking into account both the strength of their connections and the logarithm of the weights assigned to those connections.

Now, to calculate this coefficient, you could utilize a Python library called igraph, specifically utilizing its `similarity_inverse_log_weighted` function, which is designed precisely for this purpose. By integrating this function into your analysis workflow, you can gain insights into the relative similarities between different devices in the network, aiding in troubleshooting and optimizing the network's performance.
### Response:
import igraph as ig

# Create a graph with weighted edges
g = ig.Graph(edges=[(0, 1), (1, 2), (2, 3)], directed=False)

# Add edge weights
g.es['weight'] = [1, 2, 3]

# Calculate the inverse log-weighted similarity
g.similarity_inverse_log_weighted()
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