VII. Graph Neural Networks (GNNs)

In this section

• Applications of GNNs
  • Graph Classification: Predicting Graph Categories
    A detailed overview of the Graph Classification design pattern, including its implementation in Python, Java, Scala, and Clojure. This pattern is particularly useful for predicting categories of entire graphs, such as molecular property prediction.
  • Link Prediction: Predicting Missing Links in Graphs
    A detailed guide on the Link Prediction pattern in Neural Networks, covering its use in predicting missing links in graphs, typical applications in recommendation systems, and implementation examples in various programming languages.
  • Node Classification: Predicting Node Properties
    Node Classification in neural networks is used to predict properties of individual nodes, especially useful in social network analysis and other graph-related data structures.
• Types of GNNs
  • Graph Attention Networks (GAT): Utilizing Attention Mechanisms on Graph Vertices
    A detailed overview of Graph Attention Networks (GAT), including its structure, benefits, trade-offs, and practical use cases in enhancing node feature aggregation.
  • Graph Convolutional Networks (GCN): Extending Convolution to Graph Structured Data
    Graph Convolutional Networks (GCN) are a type of neural network designed to handle graph-structured data, commonly used for tasks such as node classification.
  • GraphSAGE: Graph Sample and Aggregation
    Aggregates feature information from neighbors to generate node embeddings for diverse graphs.