Graph neural network
If you can represent your data as a graph, you can use a graph neural network to perform inference on the data and output a new graph or predictions on the graph.
Introduction
Structure
A graph neural network consists of layers which operate on graphs.
At each layer, you get the following features during inference:
- The graph structure
- Node embeddings
- Edge embeddings
- Graph embeddings
Message Passing Layer
A standard GNN layer consists of pooling functions followed by update functions.
Pooling
There are many types of pooling to choose from:
- For nodes, you can add the node embedding to connected node embedding or connected edge embeddings.
- Similarly for edges, you can add its embedding to connected node embeddings.
- For the entire graph, you can add all node embeddings together.
After pooling, you can use update function (e.g. MLP) to update the embeddings/state at each node, edge, and for the entire graph.