Graph Neural Networks: Unlocking the Power of Relationships in Predictions

Author(s): Shenggang Li

Originally published on Towards AI.

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Exploring the Concepts, Types, and Real-World Applications of GNNs in Feature Selection, Economic Forecasting, and Stock Prediction

Graph Neural Networks (GNNs) are designed to work with connections and relationships. Unlike traditional deep learning models like RNNs (great for text) or CNNs (perfect for images), which handle structured data like sequences or grids, GNNs perform well on messy, complex data. Think of a social network, where people (nodes) are connected by friendships (edges), or a molecule, where bonds link atoms. GNNs don’t just focus on individual pieces; they analyze how those pieces connect and influence each other.

We’ll explore how GNNs work and what makes them so powerful. First, we’ll break down the basics of GNNs in a section called “How GNNs Understand Graphs”. From there, we’ll look at how Convolutional GNNs can be used for economic forecasting, complete with hands-on code and experiments.

Next, we’ll explore Graph Attention Networks (GATs) and their ability to pick out the most important features for making predictions. Think of feature selection as finding the most important clues in a giant puzzle — GATs are experts at understanding the relationships between those… Read the full blog for free on Medium.

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Published via Towards AI