论文标题
半监督分类的图形卷积网络组成框架
A Graph Convolutional Network Composition Framework for Semi-supervised Classification
论文作者
论文摘要
图形卷积网络（GCN）由于在包括节点分类在内的几个下游任务上可以实现的高性能而获得了知名度。这些网络的几种架构变体已通过文献实验研究进行了研究和研究。在最近的一项有关简化GCN的工作的动机，我们研究了设计其他变体的问题，并提出了一个使用GCN的构建块组成网络的框架。该框架提供了使用功能和/或标签传播网络，线性或非线性网络组成和评估不同网络的灵活性，其中每个组合都具有不同的计算复杂性。我们对具有许多变体的几个基准数据集进行了详细的实验研究，并从我们的评估中进行了观察。我们的经验实验结果表明，几种新组成的变体是有用的替代方法，因为它们与原始GCN一样具有竞争力或更好。
Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification. Several architectural variants of these networks have been proposed and investigated with experimental studies in the literature. Motivated by a recent work on simplifying GCNs, we study the problem of designing other variants and propose a framework to compose networks using building blocks of GCN. The framework offers flexibility to compose and evaluate different networks using feature and/or label propagation networks, linear or non-linear networks, with each composition having different computational complexity. We conduct a detailed experimental study on several benchmark datasets with many variants and present observations from our evaluation. Our empirical experimental results suggest that several newly composed variants are useful alternatives to consider because they are as competitive as, or better than the original GCN.
