The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction

TL;DR: This paper proposes a parameter setting method for link prediction that resorts to optimization techniques to drive the search for an adequate parameter and initialization choice.

Abstract: Link prediction is the task of social network analysis whose goal is to predict the links that will appear in the network in future instants. Among the link predictors exploiting the time evolution of the networks, we can find the tensor decomposition-based methods. A major limitation of these methods is the lack of appropriate approaches for estimating their parameters and initialization. In this paper, we address this problem by proposing a parameter setting method. Our proposed approach resorts to optimization techniques to drive the search for an adequate parameter and initialization choice.