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Link Prediction on Evolving Data Using Matrix and Tensor Factorizations

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3 Author(s)
Acar, E. ; Inf. & Decision Sci., Sandia Nat. Labs., Livermore, CA, USA ; Dunlavy, D.M. ; Kolda, T.G.

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 tensor-based 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 three-dimensional structure of temporal link data. Through several numerical experiments, we demonstrate that both matrix and tensor-based techniques are effective for temporal link prediction despite the inherent difficulty of the problem.

Published in:

Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on

Date of Conference:

6-6 Dec. 2009

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