kernels
Last tended to on September 23, 2021

Goal: we want features that characterize the structure of an entire graph.

Kernel methods are widely-used for traditional graph-level prediction. The idea is to design kernels instead of feature vectors.

That is, we want some graph feature vector $\varphi (G)$. Basically, bag-of-words for a graph, in that each node has some features, but the ordering / relation between nodes isn’t considered.