GraphSage
A representation learning method for dynamic graphs is GraphSAGE. Without retraining, it predicts the embedding of a new node using inductive learning. It uses aggregator functions to create new node embeddings based on the node’s properties and surroundings. Instead of adding them together and losing track of them, we utilize a universal aggregation function. Prior to using mean aggregation, we totaled up the messages from the neighbors and then normalized them according to the number of neighbors. Now, we can also employ deep neural networks like LSTMs or a pooling-type technique.
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.