Methods › Graphs › Graph Representation Learning › APPNP
Approximation of Personalized Propagation of Neural Predictions
APPNP
Introduced by Johannes Gasteiger et al. in Predict then Propagate: Graph Neural Networks meet Personalized PageRank
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Neural message-passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighbourhood is hard to extend. This paper uses the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters is on par or lower than previous models. It leverages a large, adjustable neighbourhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models.
Papers archive 2025-07-28
9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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LightGCN: Evaluated and Enhanced 17 Dec 2023 · 1 repository · arXiv:2312.16183
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Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective 25 Jan 2023 · 0 repositories · arXiv:2301.10536
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Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework 11 Jan 2023 · 0 repositories · arXiv:2301.04318
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MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering 5 Apr 2022 · 2 repositories · arXiv:2204.02338
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Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework 4 Mar 2021 · 1 repository · arXiv:2103.02885Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
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On the Equivalence of Decoupled Graph Convolution Network and Label Propagation 23 Oct 2020 · 1 repository · arXiv:2010.12408Syntology ran 0 of 4 samples · 4 unverified
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A Unified View on Graph Neural Networks as Graph Signal Denoising 5 Oct 2020 · 1 repository · arXiv:2010.01777Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
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Tackling Over-Smoothing for General Graph Convolutional Networks 22 Aug 2020 · 0 repositories · arXiv:2008.09864
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Predict then Propagate: Graph Neural Networks meet Personalized PageRank 14 Oct 2018 · 5 repositories · arXiv:1810.05997Syntology ran 1 of 12 samples · 11 unverified
Tasks archive 2025-07-28
14 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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