Joint Link Prediction and Attribute Inference Using a Social-Attribute Network
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).
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 01, 2014
- Source ID
- 10.1145/2594455
Entities
People
- Ameet Talwalkar
- Dawn Song
- Elaine Shi
- Emil Stefanov
- Eui Chul Richard Shin
- Lester Mackey
- Ling Huang
- Neil Zhenqiang Gong
Organizations
- Air Force Office of Scientific Research
- Air Force Research Laboratory
- Division of Computing and Communication Foundations
- Division of Graduate Education
- Intel Corporation
- National Science Foundation
- Office of Naval Research
- United States Department of Defense
- University of California, Berkeley
- University of Maryland