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

• Published in: PloS one Vol. 18; no. 8; p. e0289568
• Main Authors: Peng, Siqi, Yamamoto, Akihiro, Ito, Kimihito
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 16.08.2023; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Disease; Factorization; Links; Medicine and Health Sciences; Methods; Network analysis; Physical Sciences; Predictions; Research and Analysis Methods; Social networks; Social Sciences; Training
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0289568

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.