Graph Neural Networks
The term Graph Neural Network, in its broadest sense, refers to any Neural Network designed to take graph structured data as its input:
To cover a broader range of methods, this survey considers GNNs as all deep learning approaches for graph data.
However the original paper to propose the term specifically referred to recursive neural networks adapted to take graph-structured data as their input:
This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs.
Note, Wu et al propose a taxonomy dividing GNN's into four subgroups:
- Recurrent graph neural networks (RecGNN)
- Convolutional graph neural networks (ConvGNN)
- Graph autoencoders (GAE)
- Spatial-temporal graph neural networks (STGNN)
ConvGNN's can themselves be classified by whether they use Spectral methods or Spatial methods, and GAE's by whether they are designed for Network embedding or Graph generation.