Affiliation:
1. Sandia National Laboratories
2. National Research Institute of Electronics and Cryptology (TUBITAK-UEKAE)
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 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 matrix- and 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 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. Additionally, we show that tensor-based techniques are particularly effective for temporal data with varying periodic patterns.
Funder
U.S. Department of Energy
Publisher
Association for Computing Machinery (ACM)
Cited by
305 articles.
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