Graph ML — From 0 to Hero
Let’s understand how to create and visualize network information with Python
Graphs are fundamental data structures representing relationships between entities in various fields, including social networks, web pages, transportation networks, and academic connections. The relationships in these fields are different, and for this reason, we need to adopt different types of graphs to match the nature of these connections as closely as possible.
This article explores how to build and represent diverse graphs using Python, leveraging the NumPy and NetworkX libraries. More specifically, we use NumPy to describe connectivity structures through adjacency matrices and NetworkX to visualize these structures and understand the key differences.
Understanding the role of connectivity structures, like adjacency matrices (or similar data structures such as edge index tensors), is crucial for grasping the key ideas behind advanced graph machine learning techniques, such as Graph Neural Networks (GNNs). To build intuition about the role of adjacency matrices in GNNs, you can read the following article:
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