Functions of Graphical Neural Network
The various functions performed by the GNN are as follows:
1. Node classification
Noder classification is defined as the process of training a model to predict labels of nodes that are based on the features and specifications of the neighbor nodes and functions. It falls under the category of semi-supervised ML problems.
2. Link prediction
Link Prediction is defined as the process of defining the relationship between the two nodes in the graph. Also, this function helps to check whether the two nodes or entities are connected or not.
3. Graph classification
As the name suggests, it is used to classify the graph on the basis of nodes present on the different graphs. It is similar to that of node classification and link prediction. This function sorts the graphs into a similar category.
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.