Graph neural network

Graph with 5 nodes.

In deep learning, a graph neural network (GNN) is a subarea of neural network, which is designed to process graph structured data or data that is able to be formulated as a graph potentially (e.g. social network, polygon mesh, point cloud). Since graph data is non-Euclidean, relations between data points cannot be easily represented by their ordering when recording them, and hence standard CNN is not able to be directly applied to graph data. On the other hand, GNN not only be able to applied to non-Euclidean data but also Euclidean data such as sentences, images or videos since such data can be represented as graph data if organized properly.

Pipeline of GNN model

The design pipeline for a GNN model can be generally derived as four steps: find graph structure, specify graph, build model, and design loss functions.

The general design pipeline for a GNN model.

Find graph structure

In graph theory, a graph is denoted by:

• $V$, a set of vertices (also called nodes or points);
• $E$, a set of edges (either directed or undirected, also called links or lines).

If the input data is already in graph structure, then this step is done. Otherwise, you need to observe the data first and reorganize it to be a graph according to your requirement, while not destroying the data's property (so that your model won't face the "garbage in, garbage out" problem).