Paired Restricted Boltzmann Machine for linked data

Suhang Wang, Jiliang Tang, Fred Morstatter, Huan Liu

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

16 Scopus citations


Restricted Boltzmann Machines (RBMs) are widely adopted unsupervised representation learning methods and have powered many data mining tasks such as collaborative filtering and document representation. Recently, linked data that contains both attribute and link information has become ubiquitous in various domains. For example, social media data is inherently linked via social relations and web data is networked via hyperlinks. It is evident from recent work that link information can enhance a number of real-world applications such as clustering and recommendations. Therefore, link information has the potential to advance RBMs for better representation learning. However, the majority of existing RBMs have been designed for independent and identically distributed data and are unequipped for linked data. In this paper, we aim to design a new type of Restricted Boltzmann Machines that takes advantage of linked data. In particular, we propose a paired Restricted Boltzmann Machine (pRBM), which is able to leverage the attribute and link information of linked data for representation learning. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework pRBM.

Original languageEnglish (US)
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450340731
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Other25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Business, Management and Accounting


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