Hierarchical Attention Based Semi-supervised Network Representation Learning

摘要：

　　Network Embedding is a process of learning low-dimensional representation vectors of nodes by comprehensively utilizing network characteristics.Besides structure properties,information networks also contain rich external information,such as texts and labels.However,most of the traditional learning methods do not consider this kind of information comprehensively,which leads to the lack of semantics of embeddings.In this paper,we propose a Semi-supervised Hierarchical Attention Network Embedding method,named as SHANE,which can incorporate external information in a semi-supervised manner.First,a hierarchical attention network is used to learn the text-based embeddings according to the content of nodes.Then,the text-based embeddings and the structure-based embeddings are integrated in a closed interaction way.After that,we further introduce the label information of nodes into the embedding learning,which can promote the nodes with the same label closed in the embedding space.Extensive experiments of link prediction and node classification are conducted on two real-world datasets,and the results demonstrate that our method outperforms other comparison methods in all cases.

关键词： Network representation learning Hierarchical attention network Semi-supervised learning

作者: Jie Liu Junyi Deng Guanghui Xu Zhicheng He

作者单位: College of Computer and Control Engineering,Nankai University,Tianjin,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 237-249

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）