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Temporal Link Prediction Using Matrix and Tensor Factorizations

Temporal Link Prediction Using Matrix and Tensor Factorizations TKD00014 ACM (Typeset by SPi, Manila, Philippines) 1 of 27 March 10, 2011 Temporal Link Prediction Using Matrix and Tensor Factorizations DANIEL M. DUNLAVY and TAMARA G. KOLDA, Sandia National Laboratories EVRIM ACAR, National Research Institute of Electronics and Cryptology (TUBITAK-UEKAE) 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 article, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T + 1? If our data has underlying periodic structure, can we predict out even further in time, i.e., links at time T + 2, T + 3, etc.? In this article, we consider bipartite graphs that evolve over time and consider matrixand tensor-based methods for predicting future links. We present a weight-based method for collapsing multiyear 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Temporal Link Prediction Using Matrix and Tensor Factorizations

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1921632.1921636
Publisher site
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Abstract

TKD00014 ACM (Typeset by SPi, Manila, Philippines) 1 of 27 March 10, 2011 Temporal Link Prediction Using Matrix and Tensor Factorizations DANIEL M. DUNLAVY and TAMARA G. KOLDA, Sandia National Laboratories EVRIM ACAR, National Research Institute of Electronics and Cryptology (TUBITAK-UEKAE) 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 article, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T + 1? If our data has underlying periodic structure, can we predict out even further in time, i.e., links at time T + 2, T + 3, etc.? In this article, we consider bipartite graphs that evolve over time and consider matrixand tensor-based methods for predicting future links. We present a weight-based method for collapsing multiyear 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

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Feb 1, 2011

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