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Symmetric non-negative matrix factorization (Sym-NMF) decomposes a high-dimensional symmetric non-negative matrix into a low-dimensional non-negative matrix and has been successfully used in graph ...
Sparse matrix vector multiplications (SpMVs) are typical sparse operations which have a high ratio of memory reference volume to computations. According to the roof-line model, the performance of such ...
1 Introduction. Graph neural networks have developed rapidly in node representation learning and graph data mining in recent years. The reason why we focus on the study of graph neural networks is ...
This project, written in the C language, was created in the academic context of Advanced Data Structures, taken at the Polytechnic Institute of Cávado and Ave. The project is focused on implementing ...
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