Link Prediction Using Higher-Order Feature Combinations across Objects

Kyohei ATARASHI
Satoshi OYAMA
Masahito KURIHARA

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D    No.8    pp.1833-1842
Publication Date: 2020/08/01
Publicized: 2020/05/14
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7266
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
link prediction,  higher-order feature combinations,  bilinear model,  factorization machines,  matrix factorization,  

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Summary: 
Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.

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