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

TL;DR

This paper enhances the Social-Attribute Network framework by integrating various link prediction algorithms and demonstrates that attribute inference can improve link prediction accuracy, validated on a large-scale Google+ dataset.

Contribution

It extends the SAN framework with multiple algorithms and shows that attribute inference can improve link prediction, providing comprehensive evaluation on a new large-scale dataset.

Findings

Attribute inference improves link prediction accuracy.

Performance gains observed across multiple algorithms.

Large-scale Google+ dataset made publicly available.

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. proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with restart algorithm to the SAN framework and showed improved performance. In this paper, 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, i.e., 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.