Title: Task-Agnostic Graph Neural Explanations. 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 down-stream tasks. To address these limitations,  we propose a Task-Agnostic Graph Neural Explainer (TAGE) trained under self-supervision without knowledge about 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 while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches.