Applications of GNNs
- Graphical Neural Network is widely used in social media applications, NLP, and advertisement recommendation systems.
- It is also used for data analysis of social media applications where there is very large datasets. It helps the developer to find relationships between the data nodes generated by a specific user.
- For the use of Graph classification and Node classification.
- It is also used to make predictions in the healthcare department.
- GNN is used to establish the relationship between the user and products which increases the efficiency of a model.
GNN in Traffic
GNN is also used in the field of traffic by analyzing the speed of vehicles, the number of vehicles on the road, etc. We can predict the traffic strength using the GNN to solve complex traffic problems.
GNN in Computer Vision
Graphical Neural Network is widely used in the field of computer vision. The application of GNN in computer vision is increasing day by day rapidly. The various use case of GNN in solving computer vision is image classification, object recognition, and in the field of semantic segmentation. The advantage of using the GNN in computer vision is that it is able to establish a spatial relationship between the provided objects. GNN is able to create models which can perform the execution of complex operations between the objects of the image that allows them to create more efficient ad accurate predictions regarding any object and attributes
GNN in NLP
Talking about the GNN in the field of Natural Language Processing, in recent times, the GNN is widely chosen to be applied in the NLP. This is because the GNN is highly capable of differentiating, finding, and establishing the relationship between words and phrases, which is a prime aim of NLP. The GNN treats each word as a node in the graph and establishes a relationship between these nodes to make a semantic relationship between them. That’s how the GNN works in the field of Natural Language Processing. Making the relationship between those nodes allows the network to capture patterns and relationships between words. GNN is also used to analyze the content and then make more accurate predictions. GNN is also used to make machine learning tasks such as image classification, object recognition, and entity recognition.
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.