Variational autoencoder based bipartite network embedding by integrating local and global structure

被引:9
作者
Jiao, Pengfei [1 ,2 ]
Tang, Minghu [2 ]
Liu, Hongtao [2 ]
Wang, Yaping [2 ]
Lu, Chunyu [2 ]
Wu, Huaming [3 ]
机构
[1] Tianjin Univ, Ctr Biosafety Res & Strategy, Tianjin, Peoples R China
[2] Tianjin Univ, Coll Intelligence & Comp, Tianjin, Peoples R China
[3] Tianjin Univ, Ctr Appl Math, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
Bipartite network embedding; Local and global structure; Variational autoencoder; Nonlinear structure; DIMENSION;
D O I
10.1016/j.ins.2020.01.033
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional spaces, has a variety of applications on complex networks. Most current methods and models are not suitable for bipartite networks, which have two different types of nodes and there are no links between nodes of the same type. Furthermore, the only existing methods for bipartite network embedding ignore the internal mechanism and highly nonlinear structures of links. Therefore, in this paper, we propose a new deep learning method to learn the node embedding for bipartite networks based on the widely used autoencoder framework. Moreover, we carefully devise a node-level triplet including two types of nodes to assign the embedding by integrating the local and global structures. Meanwhile, we apply the variational autoencoder (VAE), a deep generation model with natural advantages in data generation and reconstruction, to enhance the node embedding for the highly nonlinear relationships between nodes and complex features. Experiments on some widely used datasets show the effectiveness of the proposed model and corresponding algorithm compared with some baseline network (and bipartite) embedding techniques. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:9 / 21
页数:13
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