论文标题
用于半监督节点分类的渐进图卷积网络
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
论文作者
论文摘要
图形卷积网络已成功地解决了基于图的任务，例如半监督节点分类。现有方法使用用户根据实验定义的网络结构，每层具有固定数量的层和神经元的层，并采用层面的传播规则来获取节点嵌入。设计一个自动过程来定义图形卷积网络的问题依赖性体系结构，可以极大地帮助减少训练过程中模型结构的手动设计。在本文中，我们提出了一种自动建立紧凑和特定于任务的图形卷积网络的方法。广泛使用的公开可用数据集的实验结果表明，在分类性能和网络紧凑性方面，所提出的方法优于基于卷积图网络的相关方法。
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. Existing methods use a network structure defined by the user based on experimentation with fixed number of layers and neurons per layer and employ a layer-wise propagation rule to obtain the node embeddings. Designing an automatic process to define a problem-dependant architecture for graph convolutional networks can greatly help to reduce the need for manual design of the structure of the model in the training process. In this paper, we propose a method to automatically build compact and task-specific graph convolutional networks. Experimental results on widely used publicly available datasets show that the proposed method outperforms related methods based on convolutional graph networks in terms of classification performance and network compactness.
