What are the differences between GNNs and CNNs?

What are the differences between GNNs and CNNs?

GNNs, or Graph Neural Networks, are designed to work with graph-structured data, while CNNs, or Convolutional Neural Networks, excel in processing grid-like data such as images. GNNs focus on the relationships between nodes in a graph, whereas CNNs focus on spatial hierarchies in images.

Can you give examples of where each is used?

Sure! CNNs are commonly used in image recognition, object detection, and video analysis. GNNs are often used in social network analysis, recommendation systems, and molecular chemistry.

Which one is better for image classification?

For image classification tasks, CNNs are generally better suited due to their ability to capture spatial features effectively. GNNs are not typically used for this purpose.

Are there any scenarios where GNNs outperform CNNs?

Yes, GNNs can outperform CNNs in scenarios where the data is inherently graph-structured, such as social networks or knowledge graphs, where relationships between entities are crucial.