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• W4310461505 abstract "In this article we propose a lightning fast graph embedding method called one-hot graph encoder embedding. It has a linear computational complexity and the capacity to process billions of edges within minutes on standard PC — making it an ideal candidate for huge graph processing. It is applicable to either adjacency matrix or graph Laplacian, and can be viewed as a transformation of the spectral embedding. Under random graph models, the graph encoder embedding is approximately normally distributed per vertex, and asymptotically converges to its mean. We showcase three applications: vertex classification, vertex clustering, and graph bootstrap. In every case, the graph encoder embedding exhibits unrivalled computational advantages." @default.
• W4310461505 created "2022-12-10" @default.
• W4310461505 creator A5017159636 @default.
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• W4310461505 date "2023-06-01" @default.
• W4310461505 modified "2023-10-15" @default.
• W4310461505 title "One-Hot Graph Encoder Embedding" @default.
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• W4310461505 doi "https://doi.org/10.1109/tpami.2022.3225073" @default.
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