<｜begin▁of▁sentence｜>### Instruction:
Imagine we're tasked with structuring the digital representation of a network's information architecture, specifically focusing on the relationship strength between various nodes within our system. We have a simple network; nodes labeled as 0, 1, and 2. The connections between these nodes—edges—are defined as follows: an edge connecting node 0 to node 1 with a 'strength' attribute of 1, another connecting node 0 to node 2 with a 'strength' attribute of 2, and a final edge connecting node 1 to node 2 with a 'strength' attribute of 3.

To accurately model and visualize the interconnectedness and the intensity of the relationships in our digital information system, we need to create a matrix that will serve as an adjacency matrix. However, this isn't a standard adjacency matrix; it must reflect the 'strength' attribute of each connection, providing a detailed quantification of the connection intensity.

The challenge we face is constructing and visualizing an adjacency matrix from our network data where the strength of each connection is clearly detailed. If our network information were encapsulated within a GML file, the file might be named "network-relationships.gml". Picture this matrix as a table where the cells not only identify if a connection exists but also measure the robustness of that connection based on the predefined 'strength' attributes. Could you devise a method to generate such a matrix?
### Response:
import networkx as nx
from scipy.sparse import csr_matrix

# Create a graph
G = nx.Graph()
G.add_edge(0, 1, thickness=1, weight=3)
G.add_edge(0, 2, thickness=2)
G.add_edge(1, 2, thickness=3)

# Generate an adjacency matrix based on the 'thickness' attribute
M = nx.attr_sparse_matrix(G, edge_attr='thickness', rc_order=[0, 1, 2])
csr_matrix = M.toarray()

print(csr_matrix)
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