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- W4287760246 abstract "Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph. While there are many variants of label spreading, nearly all of them are linear models, where the incoming information to a node is a weighted sum of information from neighboring nodes. Here, we add nonlinearity to label spreading through nonlinear functions of higher-order structure in the graph, namely triangles in the graph. For a broad class of nonlinear functions, we prove convergence of our nonlinear higher-order label spreading algorithm to the global solution of a constrained semi-supervised loss function. We demonstrate the efficiency and efficacy of our approach on a variety of point cloud and network datasets, where the nonlinear higher-order model compares favorably to classical label spreading, as well as hypergraph models and graph neural networks." @default.
- W4287760246 created "2022-07-26" @default.
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- W4287760246 date "2020-06-08" @default.
- W4287760246 modified "2023-09-27" @default.
- W4287760246 title "Nonlinear Higher-Order Label Spreading" @default.
- W4287760246 doi "https://doi.org/10.48550/arxiv.2006.04762" @default.
- W4287760246 hasPublicationYear "2020" @default.
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