kernels
Last modified on September 23, 2021
Links to “kernels”
Graph-level Features (Traditional Graph ML Methods > Graph-level Features)
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 \(\phi(G)\). Basically, bag-of-words for a graph, in that each node has some features, but the ordering / relation between nodes isn’t considered.