# Graph Neural Networks with Pytorch

In this story, we will dive deep into Graph neural networks (GNNs), their structure, complexities, and implementation in Python.

# What is a Graph?

Imagine a web spun not of silk, but of information. Each strand connecting data points, like pearls, becomes a vital thread in understanding complex relationships. This intricate tapestry, my friend, is what we call a **graph**.

At its core, a graph is a data structure, a way of organizing information in a clear and meaningful fashion. But unlike a simple list, a graph goes beyond mere enumeration. It captures the essence of **connections**, revealing how different pieces of data are **related** to one another.

Think of each data point as a **node**, symbolized by a circle or dot. These nodes could represent anything from people and places to objects and concepts. The true magic unfolds with **edges**, the lines that connect these nodes, signifying the **relationships** between them.

These edges can be like highways of information, some **directed**, indicating a one-way flow, like “follows” on social media. Others are **undirected**, representing a two-way connection, like “friends” on the same platform. Imagine arrows on the edges to visualize direction, if any.

The example you provided beautifully illustrates this concept. The blue circles are the **nodes**, representing individuals. The arrows, acting as **directed edges**, point from one person to another, showcasing dependencies. One person might…