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

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Silva Fernandes, Sd (Author) Other; Tork, HF (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: 2017 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2017, Tokyo, Japan, October 19-21, 2017

Pages: 99-108

Indexing

Scopus - 11 Citations

Other information

Authenticus ID: P-00N-H11

DOI: 10.1109/dsaa.2017.83

Abstract (EN): 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. © 2017 IEEE.

Language: English

Type (Professor's evaluation): Scientific