Co-regularized deep multi-network embedding

Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible. It allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts different spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages469-478
Number of pages10
ISBN (Electronic)9781450356398
DOIs
StatePublished - Apr 10 2018
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: Apr 23 2018Apr 27 2018

Publication series

NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
CountryFrance
CityLyon
Period4/23/184/27/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Fingerprint Dive into the research topics of 'Co-regularized deep multi-network embedding'. Together they form a unique fingerprint.

Cite this