Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)
Year: 2011
Type: preprint
Abstract: The eects of social inuence 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. [28, 29] 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 rst inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, largescale Google+ dataset, which we make publicly available 1 .
Source: arXiv (Cornell University)
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2100960788