DeepWalk
Deepwalk is defined as a graph neural network that is used to perform various operations on the specific structure of the target graph. Deepwalk uses a very advanced technique namely random path-traversing, that helps it to observe clearly the local structures provided in the given network. DeepWalk does this by using various random paths and after that, it trains the data using the Skip-gram model. The Skip-gram model is a machine-learning model that is capable to specify a particular node that is related to the provided input words. After recognizing the closest node related to the input, this model generates some predicted words for that specific node. This concept is used by DeepWalk.
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.