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Paired Restricted Boltzmann Machine for Linked Data

Authors:

Fred Morstatter,

Huan LiuAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1753 - 1762

https://doi.org/10.1145/2983323.2983756

Published: 24 October 2016 Publication History

Abstract

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.

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Cited By

Kumar MSingh NBiswas B(2024)PQKELP: Projected Quantum Kernel Embedding based Link Prediction in dynamic networksExpert Systems with Applications10.1016/j.eswa.2024.125944(125944)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125944
Wu LZhuang JGuo K(2024)Incremental Inductive Dynamic Network Community DetectionComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_7(93-107)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9637-7_7
Baptista ASánchez-García RBaudot ABianconi G(2023)Zoo guide to network embeddingJournal of Physics: Complexity10.1088/2632-072X/ad0e234:4(042001)Online publication date: 29-Nov-2023
https://doi.org/10.1088/2632-072X/ad0e23
Show More Cited By

Index Terms

Paired Restricted Boltzmann Machine for Linked Data
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Published: 24 October 2016

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October 24 - 28, 2016

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Kumar MSingh NBiswas B(2024)PQKELP: Projected Quantum Kernel Embedding based Link Prediction in dynamic networksExpert Systems with Applications10.1016/j.eswa.2024.125944(125944)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125944
Wu LZhuang JGuo K(2024)Incremental Inductive Dynamic Network Community DetectionComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_7(93-107)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-981-99-9637-7_7
Baptista ASánchez-García RBaudot ABianconi G(2023)Zoo guide to network embeddingJournal of Physics: Complexity10.1088/2632-072X/ad0e234:4(042001)Online publication date: 29-Nov-2023
https://doi.org/10.1088/2632-072X/ad0e23
Zhou MHan QLiu DWu Q(2023)Multi-order attribute network representation learning via constructing hierarchical graphsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-02018-x15:6(2095-2110)Online publication date: 24-Nov-2023
https://doi.org/10.1007/s13042-023-02018-x
Glisic SLorenzo B(2022)AI Algorithms in NetworksArtificial Intelligence and Quantum Computing for Advanced Wireless Networks10.1002/9781119790327.ch7(227-360)Online publication date: 15-Apr-2022
https://doi.org/10.1002/9781119790327.ch7
Jin XEom SShin SLee KHong C(2021)DORIC: discovering topological relations based on spatial link compositionKnowledge and Information Systems10.1007/s10115-021-01603-263:10(2645-2669)Online publication date: 16-Aug-2021
https://doi.org/10.1007/s10115-021-01603-2
Berahmand KNasiri ERostami MForouzandeh S(2021)A modified DeepWalk method for link prediction in attributed social networkComputing10.1007/s00607-021-00982-2Online publication date: 4-Aug-2021
https://doi.org/10.1007/s00607-021-00982-2
Bhowmick AMeneni KDanisch MGuillaume JMitra BCaverlee JHu XLalmas MWang W(2020)LouvainNEProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371800(43-51)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371800
Zhang DYin JZhu XZhang C(2020)Network Representation Learning: A SurveyIEEE Transactions on Big Data10.1109/TBDATA.2018.28500136:1(3-28)Online publication date: 1-Mar-2020
https://doi.org/10.1109/TBDATA.2018.2850013
Sun KWang LXu BZhao WTeng SXia F(2020)Network Representation Learning: From Traditional Feature Learning to Deep LearningIEEE Access10.1109/ACCESS.2020.30371188(205600-205617)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3037118
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