How to do node classification without deep learning? I have a dataset of coordinates with labels, a set of those coordinates (a graph) follows globally the same pattern.
ie : I have coordinates of eyes,mouth and nose on thousands of images. A graph can be formed from those coordinates on a single image and I want to use that structure to learn from that pattern to predict the class of the nodes (point on image).
I'd like to create machine learning model to accurately predict the class of each element.
the task is pretty simple but all references I come across are about fancy research Graph Neural Networks.
How can I do it efficiently?
That problem is commonly called graph classification. Common non-deep learning approaches for graph classification are spectral and kernel based.