Graph neural networks (GNNs) are widely utilized to capture the information spreading patterns in graphs. While remarkable performance has been achieved, there is a new trending topic of evaluating node influence. We propose a new method of evaluating node influence, which measures the prediction change of a trained GNN model caused by removing a node. A real-world application is, "In the task of predicting Twitter accounts' polarity, had a particular account been removed, how would others' polarity change?". We use the GNN as a surrogate model whose prediction could simulate the change of nodes or edges caused by node removal. To obtain the influence for every node, a straightforward way is to alternately remove every node and apply the trained GNN on the modified graph. It is reliable but time-consuming, so we need an efficient method. The related lines of work, such as graph adversarial attack and counterfactual explanation, cannot directly satisfy our needs, since they do not focus on the global influence score for every node. We propose an efficient and intuitive method, NOde-Removal-based fAst GNN inference (NORA), which uses the gradient to approximate the node-removal influence. It only costs one forward propagation and one backpropagation to approximate the influence score for all nodes. Extensive experiments on six datasets and six GNN models verify the effectiveness of NORA. Our code is available at https://github.com/weikai-li/NORA.git.

翻译：图神经网络（GNN）被广泛用于捕捉图中的信息传播模式。尽管已取得显著性能，节点影响力评估已成为新兴研究热点。我们提出一种新的节点影响力评估方法，该方法通过测量移除节点时训练好的GNN模型预测结果的变化来量化影响力。实际应用场景例如："在预测推特账号倾向性的任务中，若移除某个特定账号，其他账号的倾向性将如何变化？" 我们采用GNN作为代理模型，其预测结果可模拟节点移除导致的节点或边变化。为获取每个节点的影响力，最直接的方法是依次移除每个节点并在修改后的图上应用训练好的GNN。该方法可靠但耗时，因此需要高效方案。图对抗攻击与反事实解释等相关研究方向无法直接满足需求，因其不聚焦于全局每个节点的影响力评分。我们提出高效直观的方法——基于节点移除的快速GNN推断（NORA），通过梯度近似节点移除影响力。该方法仅需一次前向传播和一次反向传播即可近似所有节点的影响力评分。在六个数据集和六个GNN模型上的大量实验验证了NORA的有效性。我们的代码开源在https://github.com/weikai-li/NORA.git。