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The effects of social influence and homophily suggest that both networkstructure and node attribute information should inform the tasks of linkprediction and node attribute inference. Recently, Yin et al. proposedSocial-Attribute Network (SAN), an attribute-augmented social network, tointegrate network structure and node attributes to perform both link predictionand attribute inference. They focused on generalizing the random walk withrestart algorithm to the SAN framework and showed improved performance. In thispaper, we extend the SAN framework with several leading supervised andunsupervised link prediction algorithms and demonstrate performance improvementfor each algorithm on both link prediction and attribute inference. Moreover,we make the novel observation that attribute inference can help inform linkprediction, i.e., link prediction accuracy is further improved by firstinferring missing attributes. We comprehensively evaluate these algorithms andcompare them with other existing algorithms using a novel, large-scale Google+dataset, which we make publicly available.

Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)