2010/06/28 by Jérôme Kunegis, Kunegis, Jérôme, Ernesto William De Luca +5 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Graph theory and applications #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph) #cs.LG #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1006.5367
10 pages
arxiv created 2010/06/28 · openalex publication_date 2010/06/28 · arxiv updated 2010/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document--feature networks and other types of bipartite networks.