Link prediction on bipartite networks using matrix factorization with negative sample selection

We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factorization (MF), which performs well but requires reliable information on both absent and present network links as training samples. This, however, is sometimes unavailable since there is no ground truth for absent links. To solve the problem, we propose a technique called negative sample selection, which selects reliable negative training samples using formal concept analysis (FCA) of a given bipartite network in advance of the preceding MF process. We conduct experiments on two hypothetical application scenarios to prove that our joint method outperforms the raw MF-based link prediction method as well as all other previously-proposed unsupervised link prediction methods.

Automated summary

We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. This method outperforms previous unsupervised prediction methods and the raw MF-based method in two hypothetical application scenarios. The technique of negative sample selection helps in selecting reliable negative training samples in advance of the matrix factorization process, overcoming the unavailability of ground truth for absent links.