What are the applications of graph neural networks?

Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and making predictions on graph-structured data. These networks can be applied to a wide range of domains and have found applications in areas such as social network analysis, recommendation systems, drug discovery, bioinformatics, and urban planning. GNNs excel at capturing the complex relationships and dependencies present in graph data, allowing them to effectively model and predict relational patterns. For example, in recommendation systems, GNNs can learn from the interactions between users and items and offer personalized recommendations. In bioinformatics, GNNs can predict protein-protein interactions or classify biological processes based on gene expression data. Overall, the versatility of GNNs makes them a valuable tool in various domains where data is organized in graph structures.

This mind map was published on 20 August 2023 and has been viewed 56 times.