Reliable Graph Neural Network Explanations Through Adversarial Training

• URL: http://arxiv.org/abs/2106.13427v1
• Date: Fri, 25 Jun 2021 04:49:42 GMT
• Title: Reliable Graph Neural Network Explanations Through Adversarial Training
• Authors: Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, Yong Han
• Abstract summary: Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. We propose a similar training paradigm for GNNs and analyze the respective impact on a model's explanations.
• Score: 10.323055385277877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have been shown to fail in capturing a model's learned representation. Due to this problem, it is worthwhile to consider how one might train a model so that it is more amenable to post-hoc analysis. Given the success of adversarial training in the computer vision domain to train models with more reliable representations, we propose a similar training paradigm for GNNs and analyze the respective impact on a model's explanations. In instances without ground truth labels, we also determine how well an explanation method is utilizing a model's learned representation through a new metric and demonstrate adversarial training can help better extract domain-relevant insights in chemistry.

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