mlsanityData to 14 Oct 2026

Alternately Optimized Graph Neural Networks

arXiv 2206.03638 ↗First posted 8 Jun 2022cs.LGcited by 4

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Abstract (arXiv)

Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is often inefficient in computation and memory usage. In this work, we propose a new optimization framework for semi-supervised learning on graphs. The proposed framework can be conveniently solved by the alternating optimization algorithms, resulting in significantly improved efficiency. Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 43 of its 69 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

43 of 69 references.

References · 43

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31 more
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  2. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  3. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  4. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  5. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  6. 2020Simple and Deep Graph Convolutional Networkscited by 860
  7. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
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  9. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
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  14. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
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  17. 2020Towards Deeper Graph Neural Networkscited by 296
  18. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  19. 2020SIGN: Scalable Inception Graph Neural Networkscited by 284
  20. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 202
  21. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  22. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  23. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  24. 2021Interpreting and Unifying Graph Neural Networks with An Optimization Frameworkcited by 131
  25. 2020A Unified View on Graph Neural Networks as Graph Signal Denoisingcited by 110
  26. 2021Dirichlet Energy Constrained Learning for Deep Graph Neural Networkscited by 97
  27. 2020Scalable Graph Neural Networks via Bidirectional Propagationcited by 87
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  29. 2021Elastic Graph Neural Networkscited by 67
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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approachcited by 17
  2. 2023PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensationcited by 9
  3. 2023Bregman Graph Neural Networkcited by 4
  4. 2023Nonlinear Correct and Smooth for Semi-Supervised Learningcited by 0