Link Prediction for Partially Observed Networks

Yunpeng Zhao, Yun Jhong Wu, Elizaveta Levina, Ji Zhu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples, that is, edges known for certain to be absent, which creates a difficulty for existing supervised learning approaches. We develop a new method that treats the observed network as a sample of the true network with different sampling rates for positive (true edges) and negative (absent edges) examples. We obtain a relative ranking of potential links by their probabilities, using information on network topology as well as node covariates if available. The method relies on the intuitive assumption that if two pairs of nodes are similar, the probabilities of these pairs forming an edge are also similar. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein–protein interaction network and a school friendship network.

Original languageEnglish (US)
Pages (from-to)725-733
Number of pages9
JournalJournal of Computational and Graphical Statistics
Volume26
Issue number3
DOIs
StatePublished - Jul 3 2017
Externally publishedYes

Keywords

  • Link prediction
  • Ranking
  • Social networks

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Link Prediction for Partially Observed Networks'. Together they form a unique fingerprint.

Cite this