论文标题
进行图形神经网络的分析
Toward the Analysis of Graph Neural Networks
论文作者
论文摘要
Graph神经网络（GNN）最近已成为图形结构数据的强大框架。它们已应用于许多问题，例如知识图分析，社交网络建议，甚至CoVID19检测和疫苗开发。但是，与其他深层神经网络（例如饲料前向神经网络（FFNN））不同，很少有诸如验证和财产推断之类的分析可能是由于GNN的动态行为所致，而GNN的动态行为可以将任意图作为输入，而FFNN仅以固定尺寸的数字向量为输入。 本文提出了一种通过将GNN转换为FFNN并重用现有FFNN分析的方法来分析GNN的方法。我们讨论各种设计，以确保转换的可扩展性和准确性。我们说明了关于淋巴结分类案例的方法。我们认为，我们的方法为理解和分析GNN打开了新的研究方向。
Graph Neural Networks (GNNs) have recently emerged as a robust framework for graph-structured data. They have been applied to many problems such as knowledge graph analysis, social networks recommendation, and even Covid19 detection and vaccine developments. However, unlike other deep neural networks such as Feed Forward Neural Networks (FFNNs), few analyses such as verification and property inferences exist, potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs. This paper proposes an approach to analyze GNNs by converting them into FFNNs and reusing existing FFNNs analyses. We discuss various designs to ensure the scalability and accuracy of the conversions. We illustrate our method on a study case of node classification. We believe that our approach opens new research directions for understanding and analyzing GNNs.
