BiANE: Bipartite Attributed Network Embedding

• Main Authors: HUANG, Wentao, LI, Yuchen, FANG, Yuan, FAN, Ju, YANG, Hongxia
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Network embedding; Bipartite attributed network; Link prediction; Databases and Information Systems; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/5280; https://ink.library.smu.edu.sg/context/sis_research/article/6283/viewcontent/SIGIR20_BiANE.pdf
• Institution: Singapore Management University

Network embedding effectively transforms complex network data into a low-dimensional vector space and has shown great performance in many real-world scenarios, such as link prediction, node classification, and similarity search. A plethora of methods have been proposed to learn node representations and achieve encouraging results. Nevertheless, little attention has been paid on the embedding technique for bipartite attributed networks, which is a typical data structure for modeling nodes from two distinct partitions. In this paper, we propose a novel model called BiANE, short for Bipartite Attributed Network Embedding. In particular, BiANE not only models the inter-partition proximity but also models the intra-partition proximity. To effectively preserve the intra-partition proximity, we jointly model the attribute proximity and the structure proximity through a novel latent correlation training approach. Furthermore, we propose a dynamic positive sampling technique to overcome the efficiency drawbacks of the existing dynamic negative sampling techniques. Extensive experiments have been conducted on several real-world networks, and the results demonstrate that our proposed approach can significantly outperform state-of-theart methods.