Exploiting Composite Relation Graph Convolution for Attributed Network Embedding
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摘要: 网络嵌入的目的是学习网络中每个节点的低维稠密向量，该问题吸引了研究者的广泛关注.现有方法大多侧重于对图结构的建模，而忽略了属性信息.属性化网络嵌入方法虽然考虑了节点属性，但节点与属性之间的信息关系尚未得到充分的利用.提出了一种利用丰富的关系信息进行属性网络嵌入的新框架.为此，我们首先为属性网络构造节点及其属性之间的复合关系，随后提出一个复合关系图卷积网络(composite relation graph convolution network, CRGCN)模型对这2种网络中的复合关系进行编码.在真实世界的数据集上进行了广泛的实验，结果证明了该模型在多种社交网络分析的有效性.Abstract: Network embedding aims at learning a low-dimensional dense vector for each node in the network. It has attracted much attention from researchers in recent years. Most existing studies mainly focus on modeling graph structure and neglect the attribute information. Though attributed network embedding methods take node attribute into account, the informative relations between nodes and their attributes are still under-exploited. In this paper, we propose a novel framework to employ the abundant relation information for attributed network embedding. To this end, we first present to construct the composite relations between the nodes and their attributes in attributed networks. We then develop a composite relation graph convolution network (CRGCN) to encode the composite relations in both types of networks. We conduct extensive experiments on real world datasets and results demonstrate the effectiveness of our model on various network analysis tasks.
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