论文标题
Ingrex：图形神经网络的交互式解释框架
INGREX: An Interactive Explanation Framework for Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）广泛用于许多现代应用中，需要对其决策进行解释。但是，GNN的复杂性使得很难解释预测。即使最近提出了几种方法，它们也只能提供简单而静态的解释，在许多情况下，用户很难理解。因此，我们介绍了Ingrex，这是一个互动解释框架，旨在帮助用户理解模型预测。我们的框架是根据多种说明算法和高级库实施的。我们在三种情况下展示了我们的框架，涵盖了对GNN解释的共同要求，以提出其有效性和帮助。
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.
