Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them.
This article is one of two Distill publications about graph neural networks. Take a look at Understanding Convolutions on Graphs
Graphs are all around us; real world objects are often defined in ter...
Pattern analysis and deeper implications:
* The article presents a clear, concise, and accessible introduction to Graph Neural Networks (GNNs), which is valuable for researchers, practitioners, educators, and students who are interested in this field.
* By providing historical context, key concepts, applications, challenges, and future directions, the authors offer a comprehensive overview that caters to both beginners and more advanced readers.
* The article also emphasizes the importance of GN...