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Exact Age Prediction in Social Networks

Authors:

Bryan Perozzi,

Steven SkienaAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 91 - 92

https://doi.org/10.1145/2740908.2742765

Published: 18 May 2015 Publication History

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Abstract

Predicting accurate demographic information about the users of information systems is a problem of interest in personalized search, ad targeting, and other related fields. Despite such broad applications, most existing work only considers age prediction as one of classification, typically into only a few broad categories.

Here, we consider the problem of exact age prediction in social networks as one of regression. Our proposed method learns social representations which capture community information for use as covariates. In our preliminary experiments on a large real-world social network, it can predict age within 4.15 years on average, strongly outperforming standard network regression techniques when labeled data is sparse.

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Cited By

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Geva TYahav I(2021)Data-Driven Link Screening for Increasing Network PredictabilityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.295565033:6(2380-2391)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TKDE.2019.2955650
Rosales AFernández-Ardèvol M(2020)Ageism in the era of digital platformsConvergence: The International Journal of Research into New Media Technologies10.1177/1354856520962670(135485652096267)Online publication date: 14-Oct-2020
https://doi.org/10.1177/1354856520962670
Rosales AFernández-Ardèvol M(2020)Ageism in the era of digital platformsConvergence: The International Journal of Research into New Media Technologies10.1177/135485652093090526:5-6(1074-1087)Online publication date: 25-Jun-2020
https://doi.org/10.1177/1354856520930905
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Index Terms

Exact Age Prediction in Social Networks
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

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Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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WWW '15

Sponsor:

IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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Geva TYahav I(2021)Data-Driven Link Screening for Increasing Network PredictabilityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.295565033:6(2380-2391)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TKDE.2019.2955650
Rosales AFernández-Ardèvol M(2020)Ageism in the era of digital platformsConvergence: The International Journal of Research into New Media Technologies10.1177/1354856520962670(135485652096267)Online publication date: 14-Oct-2020
https://doi.org/10.1177/1354856520962670
Rosales AFernández-Ardèvol M(2020)Ageism in the era of digital platformsConvergence: The International Journal of Research into New Media Technologies10.1177/135485652093090526:5-6(1074-1087)Online publication date: 25-Jun-2020
https://doi.org/10.1177/1354856520930905
Rozemberczki BSarkar Rd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411866(1325-1334)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411866
Rosales AFernández-Ardèvol M(2019)Structural Ageism in Big Data ApproachesNordicom Review10.2478/nor-2019-001340:s1(51-64)Online publication date: 28-Jun-2019
https://doi.org/10.2478/nor-2019-0013
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Epasto APerozzi B(2019)Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social ContextsThe World Wide Web Conference10.1145/3308558.3313660(394-404)Online publication date: 13-May-2019
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Wu CWu FLiu JHe SHuang YXie XCulpepper JMoffat ABennett PLerman K(2019)Neural Demographic Prediction using Search QueryProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291034(654-662)Online publication date: 30-Jan-2019
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Luo XXie MZhang KZhou FZhong T(2019)DeepAttr: Inferring Demographic Attributes via Social Network EmbeddingIEEE Access10.1109/ACCESS.2019.29407527(130270-130282)Online publication date: 2019
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