Graph Neural Network
Introduction to Graph Neural Networks Graph neural networks (GNNs) are a class of deep learning models designed specifically for handling graph-structured data. Unlike standard neural networks which operate on vectors, GNNs can operate directly on more complex data such as graphs and networks. This makes GNNs uniquely suited for analyzing relationships and connections within data. In recent years, GNNs have seen rapidly rising popularity and importance across various fields including social network analysis, recommender systems, drug discovery, and more. Their ability to capture complex relational information makes them applicable to any domain where the relationships between data points matter. Modeling Relational Data A key advantage of GNNs is their innate ability to model relational data . Graphs can naturally represent any data that has some sort of relationship between elements – for example, a social network graph models relationships between people. GNNs...