Task-Agnostic Graph Explanations
- URL: http://arxiv.org/abs/2202.08335v1
- Date: Wed, 16 Feb 2022 21:11:47 GMT
- Title: Task-Agnostic Graph Explanations
- Authors: Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao,
Karthik Subbian, Shuiwang Ji
- Abstract summary: Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data.
Existing learning-based GNN explanation approaches are task-specific in training.
We propose a Task-Agnostic GNN Explainer (TAGE) trained under self-supervision with no knowledge of downstream tasks.
- Score: 50.17442349253348
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph
structured data. Due to their broad applications, there is an increasing need
to develop tools to explain how GNNs make decisions given graph structured
data. Existing learning-based GNN explanation approaches are task-specific in
training and hence suffer from crucial drawbacks. Specifically, they are
incapable of producing explanations for a multitask prediction model with a
single explainer. They are also unable to provide explanations in cases where
the GNN is trained in a self-supervised manner, and the resulting
representations are used in future downstream tasks. To address these
limitations, we propose a Task-Agnostic GNN Explainer (TAGE) trained under
self-supervision with no knowledge of downstream tasks. TAGE enables the
explanation of GNN embedding models without downstream tasks and allows
efficient explanation of multitask models. Our extensive experiments show that
TAGE can significantly speed up the explanation efficiency by using the same
model to explain predictions for multiple downstream tasks while achieving
explanation quality as good as or even better than current state-of-the-art GNN
explanation approaches.
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