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

Neil Zhenqiang Gong

doi:10.1145/2594455

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

2014-04-30

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Neil Zhenqiang Gong *

DOI：10.1145/2594455

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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

Keyword:

Algorithms

Measurement

Link prediction

attribute inference

social-attribute network

heterogeneousnetwork

Google

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

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