Types of Graph Neural Network
The GNN is classified into 3 types that are as follows:
1. Recurrent GNN
It is a type of GNN in which the connection between the various nodes generates a cyclic allowance of output from the other nodes. This affects the node of the same cycle from the output and it behaves dynamically in nature. It is based on a Banach Fixed-Point Theorem.
2. Spatial GNN
Spatial GNN is similar to CNN. It also performs on the basis of collecting the properties of its neighboring nodes and transferring them to the central node.
3. Spectral GNN
This GNN is based on the matric theory of mathematics. Like other GNNs uses the nodes and topology to extract the result but spectral GNN uses the eigenvalues and eigenvector concepts to get the result from the provided graphical data.
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.