Semi-supervised Learning on Network Using Structure Features and Graph Convolution
Semi-supervised Learning on Network Using Structure Features and Graph Convolution
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DOI：
10.1527/tjsai.b-ic2
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发表时间：
2019-09
影响因子：
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通讯作者：
M. Tachibana;T. Murata
中科院分区：
文献类型：
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作者：
M. Tachibana;T. Murata
由于几种类型的数据可以表示为图形，因此一直需要将神经网络模型推广到图形数据。图卷积是一种最近的可扩展方法，用于通过聚合多层上的本地节点信息来对属性图执行深度特征学习。这些层只考虑前向模型中节点邻居的属性信息，而不将全局网络结构的知识纳入学习任务中。本文提出了一种适用于图结构数据的可扩展的半监督学习方法，
: Since several types of data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. Graph convolution is a recent scalable method for performing deep feature learning on attributed graphs by aggregating local node information over multiple layers. Such layers only consider attribute information of node neighbors in the forward model and do not incorporate knowledge of global network structure in the learning task. In this paper, we present a scalable semi-supervised learning method for graph-structured data which considers not only neighbors information