mlsanityData to 14 Oct 2026

Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery

arXiv 2405.12797 ↗First posted 21 May 2024cs.SI stat.MLcited by 5

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Abstract (arXiv)

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful hidden communities under stochastic block models. Furthermore, we show how the refinement method leads to improved vertex embedding and better decision boundaries for subsequent vertex classification. The efficacy of our approach is validated through numerical experiments, which exhibit clear advantages in identifying meaningful latent communities and improved vertex classification across a collection of simulated and real-world graph data.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 21 May 2024.

Builds on

27 of 60 references.

References · 27

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2018How Powerful are Graph Neural Networks?cited by 4,393
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  6. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  7. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  8. 2015LINE: Large-scale Information Network Embeddingcited by 582
  9. 2017Cooperative Game Theory Approaches for Network Partitioningcited by 135
  10. 2020Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Modelscited by 116
  11. 2017Statistical inference on random dot product graphs: a surveycited by 70
  12. 2017A statistical interpretation of spectral embedding: the generalised random dot product graphcited by 55
15 more
  1. 2019Inference for multiple heterogeneous networks with a common invariant subspacecited by 39
  2. 2023Graph Positional Encoding via Random Feature Propagationcited by 31
  3. 2018On a 'Two Truths' Phenomenon in Spectral Graph Clusteringcited by 24
  4. 2021One-Hot Graph Encoder Embeddingcited by 16
  5. 2019GraSPy: Graph Statistics in Pythoncited by 15
  6. 2021Spectral embedding for dynamic networks with stability guaranteescited by 13
  7. 2019Spectral embedding of weighted graphscited by 9
  8. 2023Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detectioncited by 8
  9. 2013Generalized Canonical Correlation Analysis for Classificationcited by 7
  10. 2022Simulating and Modeling the Risk of Conversational Searchcited by 6
  11. 2023Synergistic Graph Fusion via Encoder Embeddingcited by 6
  12. 2023Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamicscited by 5
  13. 2019Community Correlations and Testing Independence Between Binary Graphscited by 4
  14. 2020On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariatescited by 4
  15. 2020Vertex nomination between graphs via spectral embedding and quadratic programmingcited by 3

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024Encoder Embedding for General Graph and Node Classificationcited by 4
  2. 2025Principal Graph Encoder Embedding and Principal Community Detectioncited by 2
  3. 2025A Graph Sufficiency Perspective for Neural Networkscited by 1
  4. 2024Fast and Scalable Multi-Kernel Encoder Classifiercited by 0
  5. 2025Graph Neural Networks Powered by Encoder Embedding for Improved Node Learningcited by 0