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...

• Type: Journal
• Year: 2009
• Where: ICDM
• Authors: Evrim Acar, Daniel M. Dunlavy, Tamara G. Kolda
• Keywords: Data Mining, ICDM 2009, Link Data, Link Prediction, Temporal Link Prediction