Challenges of using GNN in Machine Learning
Although GNNs are useful in solving machine learning problems, there are some challenges and limitations of GNN in ML which are as follows:
- A GNN uses the features of neighbor nodes to predict the central node, and it continues until the node becomes stable.
- GNN uses a huge amount of data to learn.
- It adjusts the parameter to minimize the loss function of the model.
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.