mlsanityData to 14 Oct 2026

Personalized PageRank Graph Attention Networks

arXiv 2205.14259 ↗First posted 27 May 2022cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

There has been a rising interest in graph neural networks (GNNs) for representation learning over the past few years. GNNs provide a general and efficient framework to learn from graph-structured data. However, GNNs typically only use the information of a very limited neighborhood for each node to avoid over-smoothing. A larger neighborhood would be desirable to provide the model with more information. In this work, we incorporate the limit distribution of Personalized PageRank (PPR) into graph attention networks (GATs) to reflect the larger neighbor information without introducing over-smoothing. Intuitively, message aggregation based on Personalized PageRank corresponds to infinitely many neighborhood aggregation layers. We show that our models outperform a variety of baseline models for four widely used benchmark datasets. Our implementation is publicly available online.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 27 May 2022.

Builds on

15 of 22 references.

References · 15

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  5. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  7. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  8. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  9. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  10. 2021How Attentive are Graph Attention Networks?cited by 835
  11. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 584
  12. 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
3 more
  1. 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
  2. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  3. 2017Predicting multicellular function through multi-layer tissue networkscited by 138

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2023Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Predictioncited by 8
  2. 2026GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operatorscited by 4
  3. 2022EGRC-Net: Embedding-induced Graph Refinement Clustering Networkcited by 3
  4. 2025Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIncited by 0