Taxonomy of Benchmarks in Graph Representation Learning. (arXiv:2206.07729v1 [cs.LG])

Graph Neural Networks (GNNs) extend the success of neural networks to
graph-structured data by accounting for their intrinsic geometry. While
extensive research has been done on developing GNN models with superior
performance according to a collection of graph representation learning
benchmarks, it is currently not well understood what aspects of a given model
are probed by them. For example, to what extent do they test the ability of a
model to leverage graph structure vs. node features? Here, we develop …

arxiv benchmarks graph graph representation learning lg representation representation learning taxonomy