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2009
IEEE

Link Prediction on Evolving Data Using Matrix and Tensor Factorizations

8 years 8 months ago
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
Abstract--The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for time periods 1 through T, can we predict the links in time period T +1? Specifically, we look at bipartite graphs changing over time and consider matrix- and tensorbased methods for predicting links. We present a weight-based method for collapsing multi-year data into a single matrix. We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition. Using a CANDECOMP/PARAFAC tensor decomposition of the data, we illustrate the usefulness of exploiting the natural threedimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrixand tensor-based technique...
Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICDM
Authors Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda
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