{"ID":2885548,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04077","arxiv_id":"2508.04077","title":"The Ubiquitous Sparse Matrix-Matrix Products","abstract":"Multiplication of a sparse matrix with another (dense or sparse) matrix is a fundamental operation that captures the computational patterns of many data science applications, including but not limited to graph algorithms, sparsely connected neural networks, graph neural networks, clustering, and many-to-many comparisons of biological sequencing data. In many application scenarios, the matrix multiplication takes places on an arbitrary algebraic semiring where the scalar operations are overloaded with user-defined functions with certain properties or a more general heterogenous algebra where even the domains of the input matrices can be different. Here, we provide a unifying treatment of the sparse matrix-matrix operation and its rich application space including machine learning, computational biology and chemistry, graph algorithms, and scientific computing.","short_abstract":"Multiplication of a sparse matrix with another (dense or sparse) matrix is a fundamental operation that captures the computational patterns of many data science applications, including but not limited to graph algorithms, sparsely connected neural networks, graph neural networks, clustering, and many-to-many comparison...","url_abs":"https://arxiv.org/abs/2508.04077","url_pdf":"https://arxiv.org/pdf/2508.04077v1","authors":"[\"Aydın Buluç\"]","published":"2025-08-06T04:26:52Z","proceeding":"math.NA","tasks":"[\"math.NA\",\"cs.DC\",\"cs.LG\",\"cs.MS\",\"math.CO\"]","methods":"[\"Graph Neural Network\"]","has_code":false}