Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

Abstract

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.

Keywords

Network embedding, Bipartite attributed network, Link prediction

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual event, China, 2020 July 25-30

First Page

149

Last Page

158

ISBN

978145038016420

Identifier

10.1145/3397271.3401068

Publisher

ACM

City or Country

Virtual Event, China

Additional URL

https://doi.org/10.1145/3397271.3401068

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