INGREX: An Interactive Explanation Framework for Graph Neural Networks

• URL: http://arxiv.org/abs/2211.01548v1
• Date: Thu, 3 Nov 2022 01:47:33 GMT
• Title: INGREX: An Interactive Explanation Framework for Graph Neural Networks
• Authors: Tien-Cuong Bui, Van-Duc Le, Wen-Syan Li, Sang Kyun Cha
• Abstract summary: Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. We introduce INGREX, an interactive explanation framework for GNNs designed to aid users in comprehending model predictions.
• Score: 0.17842332554022688
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. However, the complexity of GNNs makes it difficult to explain predictions. Even though several methods have been proposed lately, they can only provide simple and static explanations, which are difficult for users to understand in many scenarios. Therefore, we introduce INGREX, an interactive explanation framework for GNNs designed to aid users in comprehending model predictions. Our framework is implemented based on multiple explanation algorithms and advanced libraries. We demonstrate our framework in three scenarios covering common demands for GNN explanations to present its effectiveness and helpfulness.

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