research-article

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

Authors:

Neil Zhenqiang Gong,

Ameet Talwalkar,

Eui Chul Richard Shin,

Elaine (Runting) Shi,

Dawn SongAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 2

Article No.: 27, Pages 1 - 20

https://doi.org/10.1145/2594455

Published: 30 April 2014 Publication History

Abstract

The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call Social-Attribute Network (SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Google+ dataset, which we make publicly available (http://www.cs.berkeley.edu/~stevgong/gplus.html).

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Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 2

Special Issue on Linking Social Granularity and Functions

April 2014

347 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2611448

Issue’s Table of Contents

Copyright © 2014 ACM.

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Publication History

Published: 30 April 2014

Accepted: 01 March 2013

Revised: 01 February 2013

Received: 01 October 2012

Published in TIST Volume 5, Issue 2

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Division of Computing and Communication Foundations
Intel Corporation
Air Force Research Laboratory
National Science Foundation
grant from the Amazon Web Services in Education program
Air Force Office of Scientific Research
U.S. Department of Defense
Office of Naval Research
Division of Graduate Education

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https://doi.org/10.1109/TNNLS.2022.3193564
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