How testing (new data points) works in graph neural network In machine learning, data is divided into train and test splits. The machine learns weights using training data and we can utilize weights to predict test data.
Similarly, we are also learning weights in GNN. But how is testing performed for new or test data points? The crux behind GNN is its connection of edges and nodes for data points. Given a new data point or test point (let's say for a single point), it does not have any connection with other training data. How does testing work in GNN?
Do we have to give training and testing data both while training itself? What is an efficient way to perform testing?
I am a newbie to GNN.

For GNNs operating on data where every training/test sample is a separate graph, this is not a problem. Examples include chemical compounds, brain networks etc.
For GNNs operating on large graphs which can evolve through time, like social network, there are approaches to address this problem, by introducing an inductive learning framework by aggregating attributes from the neighbors of the new node. Example of such an approach is GraphSAGE (introduced in a NIPS publication in 2017).
