What is Graph Neural Network?
Graph Neural Networks were developed by Scarselli et al. in 2008. The GNN was developed to fulfill the purpose of modeling dynamic systems using graphical data. As soon as the GNN developed it attracted the eyes of researchers and it became a major area of research and applications. There is a wide range of GNN applications such as social media applications, marketing, etc. Graph Neural Networks are a methodology that is used to solve complex graph-related problems. As we know, a graph represents objects and their relationship using mathematical formulas. There are two parameters of a graph. The first is an object (node) and the second is a relationship (edges). Let’s take an example of a social network that can be represented as a graph, where the users are the nodes, and their connections are the edges. The prime focus for developing the GNN was that it will be able to establish and learn the hidden patterns and relationships for the graphical data. Traditional neural networks are not capable of handling graph-structured data because these models suppose that the input data is of a fixed-size vector.
What are Graph Neural Networks?
Graph Neural Network is a modern machine learning technique that is sued to perform various operations on graphical data. There are traditional neural networks already available for analyzing, and performing operations but they are limited to textual data only. When we need to tackle the graphical data, we use the Graph Neural Network. Graphical neural networks let you examine these connections in novel ways because graphs are strong data structures that store relationships between items. A GNN can be utilized, for instance, to identify which users are most likely to endorse something on social networking sites. This article will explore the basics of Graph Neural Networks, along with the architecture of GNN, and how they work. We will also discuss the applications of GNNs and their limitations.