A marginalized denoising method for link prediction in relational data

Zheng Chen, Weixiong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations


Missing information is ubiquitous in relational datasets. Imputation of missing relations, a.k.a. link prediction, has become an increasingly crucial problem in relational data analysis as a huge amount of data has been accumulated in various fields. Recent advances in the latent variable models have greatly improved the state-of-the-art in the link prediction accuracy, however it comes at the price of increasing complexity. In this paper, we propose a novel link prediction algorithm, marginalized denoising model (MDM), where the problem of predicting unobserved or missing links in a given relational matrix is cast as a problem of matrix denoising. The method learns a mapping function that models the embedded topological structures of the relational network by capturing the so-called indirect affinities among entities. We train the mapping function by recovering the originally observed matrix from a conceptually "infinite" number of corrupted matrices where some links are randomly masked from the observed matrix. By re-applying the learned function to the observed relational matrix, we aim to "denoise" the observed matrix and thus to recover the unobserved links. Experimental results on several benchmarks demonstrate the superior performance of the new method over several stateof-the-art link prediction methods.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811515
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014


Conference14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States

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