<｜begin▁of▁sentence｜>### Instruction:
As a travel writer, you're currently exploring a new destination and participating in a travel networking event. While conversing with a group of local network engineers, you become intrigued by a conversation about network representation and graph analysis. The engineers are discussing the networkx library and its capabilities in visualizing and analyzing network data.

Intrigued by this conversation, you decide to write an article about applying network analysis to understand local transportation networks. While researching for your article, you come across a custom `Matrix` class that can be initialized with a graph's adjacency matrix. You wonder about the purpose of using a matrix representation of a transportation network and how it could be applicable to your experience.

Subsequently, you aim to learn more about the process of initializing a custom `Matrix` class with the adjacency matrix of a graph, and gather insights into the benefits of using a matrix representation of a transportation network for analysis and visualization purposes.
### Response:
from networkx import Graph
from networkx.linalg.graphmatrix import adjacency_matrix

# Create a graph instance in NetworkX
g = Graph([(1, 2), (1, 3), (3, 4)])

# Generate adjacency matrix using NetworkX
adj_matrix = adjacency_matrix(g).todense()

# Assume we have a custom `Matrix` class based on the API doc
class Matrix:
    def __init__(self, data=None):
        if data is None:
            # Create a 0x0 matrix if no data is provided
            self.data = []
        else:
            self.data = data

        # You can add more methods to this Matrix class as needed

# Initialize our Matrix with the adjacency matrix data
matrix_instance = Matrix(adj_matrix.tolist())

# Display the matrix data
print('Matrix data:', matrix_instance.data)
<|EOT|>
