Task-Agnostic Graph Neural Network Evaluation via Adversarial
Collaboration
- URL: http://arxiv.org/abs/2301.11517v3
- Date: Mon, 27 Mar 2023 02:59:10 GMT
- Title: Task-Agnostic Graph Neural Network Evaluation via Adversarial
Collaboration
- Authors: Xiangyu Zhao, Hannes St\"ark, Dominique Beaini, Yiren Zhao, Pietro
Li\`o
- Abstract summary: GraphAC is a principled, task-agnostic, and stable framework for evaluating Graph Neural Network (GNN) research for molecular representation learning.
We introduce a novel objective function: the Competitive Barlow Twins, that allow two GNNs to jointly update themselves from direct competitions against each other.
- Score: 11.709808788756966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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