mlsanityData to 7 Oct 2026

Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning

arXiv 2206.12933 ↗First posted 26 Jun 2022cs.LGcited by 13

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Abstract (arXiv)

Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs. Existing methods can be roughly divided into predictive learning and contrastive learning, where the latter one attracts more research attention with better empirical performance. We argue that, however, predictive models weaponed with powerful decoder could achieve comparable or even better representation power than contrastive models. In this work, we propose a Wiener Graph Deconvolutional Network (WGDN), an augmentation-adaptive decoder empowered by graph wiener filter to perform information reconstruction. Theoretical analysis proves the superior reconstruction ability of graph wiener filter. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 43 of its 58 references that are in the map. First posted on arXiv 26 Jun 2022.

Builds on

43 of 58 references.

References · 43

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31 more
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Cited by year

Citing papers per year: 2023: 3; 2024: 5; 2025: 5.

13 papers.

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  1. 2023All in One: Multi-task Prompting for Graph Neural Networkscited by 147
  2. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  3. 2024ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphscited by 83
  4. 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 60
  5. 2024ProG: A Graph Prompt Learning Benchmarkcited by 29
  6. 2023SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biasescited by 27
  7. 2024Deep Reinforcement Learning for Modelling Protein Complexescited by 5
  8. 2024Heterophilic Graph Neural Networks Optimization with Causal Message-passingcited by 4
  9. 2025Graph Positional Autoencoders as Self-supervised Learnerscited by 3
  10. 2025Heterogeneous Graph Prompt Learning via Adaptive Weight Pruningcited by 1
  11. 2025GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)cited by 1
  12. 2025Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detectioncited by 0
1 more
  1. 2025IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencodercited by 0