It has been increasingly demanding to develop reliable methods to evaluate
the progress of Graph Neural Network (GNN) research for molecular
representation learning. Existing GNN benchmarking methods for molecular
representation learning focus on comparing the GNNs' performances on some
node/graph classification/regression tasks on certain datasets. However, there
lacks a principled, task-agnostic method to directly compare two GNNs.
Additionally, most of the existing self-supervised learning works incorporate
handcrafted augmentations to the data, which has several severe difficulties to
be applied on graphs due to their unique characteristics. To address the
aforementioned issues, we propose GraphAC (Graph Adversarial Collaboration) --
a conceptually novel, principled, task-agnostic, and stable framework for
evaluating GNNs through contrastive self-supervision. We introduce a novel
objective function: the Competitive Barlow Twins, that allow two GNNs to
jointly update themselves from direct competitions against each other. GraphAC
succeeds in distinguishing GNNs of different expressiveness across various
aspects, and has demonstrated to be a principled and reliable GNN evaluation
method, without necessitating any augmentations.

提出了一个新的基于竞争式自监督的 GraphAC 框架，可以在无需手工增强的情况下，比较不同 GNNs 的表现， 消除了已有方法局限性。 competitive Barlow twins 作为一个新的目标函数，可以让两个 GNNs 在相互竞争中同步更新，成功区分了不同表现的 GNNs。