论文标题
通过接收场分布匹配图缩合
Graph Condensation via Receptive Field Distribution Matching
论文作者
论文摘要
图形神经网络（GNNS）可以使用深度学习对图进行分析，并在图中捕获结构化信息的结果有希望的结果。本文着重于创建一个小图来表示原始图，以便在尺寸降低的图上训练的GNN可以做出准确的预测。我们将原始图视为接收场的分布，并旨在综合一个小图，该图形具有相似的分布。因此，我们通过接受场分布匹配（GCDM）提出图形屈服，这是通过使用最大平均差异（MMD）量化的分布匹配损耗来优化合成图来完成的。此外，我们证明了GCDM生成的合成图在评估阶段高度推广到各种模型，并且使用此框架可显着提高冷凝速度。
Graph neural networks (GNNs) enable the analysis of graphs using deep learning, with promising results in capturing structured information in graphs. This paper focuses on creating a small graph to represent the original graph, so that GNNs trained on the size-reduced graph can make accurate predictions. We view the original graph as a distribution of receptive fields and aim to synthesize a small graph whose receptive fields share a similar distribution. Thus, we propose Graph Condesation via Receptive Field Distribution Matching (GCDM), which is accomplished by optimizing the synthetic graph through the use of a distribution matching loss quantified by maximum mean discrepancy (MMD). Additionally, we demonstrate that the synthetic graph generated by GCDM is highly generalizable to a variety of models in evaluation phase and that the condensing speed is significantly improved using this framework.
