个人信息Personal Information
副教授
博士生导师
硕士生导师
性别:女
毕业院校:大连理工大学
学位:博士
所在单位:软件学院、国际信息与软件学院
办公地点:开发区校区综合楼317
电子邮箱:BoXu@dlut.edu.cn
DINE: A Framework for Deep Incomplete Network Embedding
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论文类型:会议论文
发表时间:2019-12-02
收录刊物:EI
卷号:11919 LNAI
页面范围:165-176
摘要:Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines. ? 2019, Springer Nature Switzerland AG.