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arXiv · 2109.13098

One-Hot Graph Encoder Embedding

Cencheng Shen·Qizhe Wang·Carey E. Priebe
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Abstract

In this paper 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.

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BibTeXRIS

Cencheng Shen, Qizhe Wang, Carey E. Priebe. 2021-09-27. One-Hot Graph Encoder Embedding. https://doi.org/10.1109/tpami.2022.3225073

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