mlsanityData to 7 Oct 2026

One-Hot Graph Encoder Embedding

arXiv 2109.13098 ↗First posted 27 Sep 2021cs.LG stat.MLcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 10 of its 32 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

10 of 32 references.

References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  4. 2020Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Modelscited by 116
  5. 2015Achieving Optimal Misclassification Proportion in Stochastic Block Modelcited by 35
  6. 2018On a 'Two Truths' Phenomenon in Spectral Graph Clusteringcited by 24
  7. 2013Subsampling bootstrap of count features of networkscited by 8
  8. 2017Bootstrapping Exchangeable Random Graphscited by 6
  9. 2013Robust Vertex Classificationcited by 2
  10. 2017Network Dependence Testing via Diffusion Maps and Distance-Based Correlationscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 5; 2026: 1.

16 papers.

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  1. 2022From Local to Global: Spectral-Inspired Graph Neural Networkscited by 11
  2. 2023Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detectioncited by 8
  3. 2023Synergistic Graph Fusion via Encoder Embeddingcited by 6
  4. 2023Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamicscited by 5
  5. 2024Refined Graph Encoder Embedding via Self-Training and Latent Community Recoverycited by 5
  6. 2024Encoder Embedding for General Graph and Node Classificationcited by 4
  7. 2025Principal Graph Encoder Embedding and Principal Community Detectioncited by 2
  8. 2023Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embeddingcited by 1
  9. 2024Edge-Parallel Graph Encoder Embeddingcited by 1
  10. 2024Efficient Graph Encoder Embedding for Large Sparse Graphs in Pythoncited by 1
  11. 2025A Graph Sufficiency Perspective for Neural Networkscited by 1
  12. 2024Fast and Scalable Multi-Kernel Encoder Classifiercited by 0
4 more
  1. 2025Explaining Categorical Feature Interactions Using Graph Covariance and LLMscited by 0
  2. 2025Graph Transformer with Disease Subgraph Positional Encoding for Improved Comorbidity Predictioncited by 0
  3. 2025Graph Neural Networks Powered by Encoder Embedding for Improved Node Learningcited by 0
  4. 2026Adjacency Spectral Embeddings of Correlation Networkscited by 0