  Graph learning methods have been extensively applied in diverse application
areas. However, what kind of inherent graph properties e.g. graph proximity,
graph structural information has been encoded into graph representation
learning for downstream tasks is still under-explored. In this paper, we
propose a novel graph probing framework (GraphProbe) to investigate and
interpret whether the family of graph learning methods has encoded different
levels of knowledge in graph representation learning. Based on the intrinsic
properties of graphs, we design three probes to systematically investigate the
graph representation learning process from different perspectives, respectively
the node-wise level, the path-wise level, and the structural level. We
construct a thorough evaluation benchmark with nine representative graph
learning methods from random walk based approaches, basic graph neural networks
and self-supervised graph methods, and probe them on six benchmark datasets for
node classification, link prediction and graph classification. The experimental
evaluation verify that GraphProbe can estimate the capability of graph
representation learning. Remaking results have been concluded: GCN and
WeightedGCN methods are relatively versatile methods achieving better results
with respect to different tasks.
