  Graph pre-training strategies have been attracting a surge of attention in
the graph mining community, due to their flexibility in parameterizing graph
neural networks (GNNs) without any label information. The key idea lies in
encoding valuable information into the backbone GNNs, by predicting the masked
graph signals extracted from the input graphs. In order to balance the
importance of diverse graph signals (e.g., nodes, edges, subgraphs), the
existing approaches are mostly hand-engineered by introducing hyperparameters
to re-weight the importance of graph signals. However, human interventions with
sub-optimal hyperparameters often inject additional bias and deteriorate the
generalization performance in the downstream applications. This paper addresses
these limitations from a new perspective, i.e., deriving curriculum for
pre-training GNNs. We propose an end-to-end model named MentorGNN that aims to
supervise the pre-training process of GNNs across graphs with diverse
structures and disparate feature spaces. To comprehend heterogeneous graph
signals at different granularities, we propose a curriculum learning paradigm
that automatically re-weighs graph signals in order to ensure a good
generalization in the target domain. Moreover, we shed new light on the problem
of domain adaption on relational data (i.e., graphs) by deriving a natural and
interpretable upper bound on the generalization error of the pre-trained GNNs.
Extensive experiments on a wealth of real graphs validate and verify the
performance of MentorGNN.
