mlsanityData to 14 Oct 2026

GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

arXiv 2406.02953 ↗First posted 5 Jun 2024cs.LGcited by 15

Not on the map. Why?

Abstract (arXiv)

Graph self-supervised learning (SSL) holds considerable promise for mining and learning with graph-structured data. Yet, a significant challenge in graph SSL lies in the feature discrepancy among graphs across different domains. In this work, we aim to pretrain one graph neural network (GNN) on a varied collection of graphs endowed with rich node features and subsequently apply the pretrained GNN to unseen graphs. We present a general GraphAlign method that can be seamlessly integrated into the existing graph SSL framework. To align feature distributions across disparate graphs, GraphAlign designs alignment strategies of feature encoding, normalization, alongside a mixture-of-feature-expert module. Extensive experiments show that GraphAlign empowers existing graph SSL frameworks to pretrain a unified and powerful GNN across multiple graphs, showcasing performance superiority on both in-domain and out-of-domain graphs.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 41 of its 50 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

41 of 50 references.

References · 41

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2017Graph Attention Networkscited by 8,923
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  8. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  9. 2018How Powerful are Graph Neural Networks?cited by 4,393
  10. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  11. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  12. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
29 more
  1. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  2. 2019Simplifying Graph Convolutional Networkscited by 1,477
  3. 2016Variational Graph Auto-Encoderscited by 1,382
  4. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  5. 2018Deep Graph Infomaxcited by 1,106
  6. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  7. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  8. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  9. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  10. 2020Deep Graph Contrastive Representation Learningcited by 539
  11. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  12. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 463
  13. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 448
  14. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  15. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 303
  16. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  17. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 243
  18. 2021Large-Scale Representation Learning on Graphs via Bootstrappingcited by 186
  19. 2023All in One: Multi-task Prompting for Graph Neural Networkscited by 148
  20. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 141
  21. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 133
  22. 2023GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learnercited by 132
  23. 2023PRODIGY: Enabling In-context Learning Over Graphscited by 98
  24. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  25. 2020Graph Prototypical Networks for Few-shot Learning on Attributed Networkscited by 90
  26. 2022Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discriminationcited by 69
  27. 2022Task-Adaptive Few-shot Node Classificationcited by 43
  28. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  29. 2022Graph Few-shot Learning with Task-specific Structurescited by 26

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 5.

15 papers.

Sort:

By area:

  1. 2025UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphscited by 49
  2. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  3. 2024One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphscited by 12
  4. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigationcited by 12
  5. 2024Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Taskscited by 10
  6. 2025Model Generalization on Text Attribute Graphs: Principles with Large Language Modelscited by 8
  7. 2025Towards Graph Foundation Models: A Transferability Perspectivecited by 7
  8. 2025BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactionscited by 4
  9. 2026Modality-free Graph In-context Alignmentcited by 3
  10. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  11. 2025RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphscited by 1
  12. 2026Generalist Graph Anomaly Detection via Prototype-Based Distillationcited by 1
3 more
  1. 2026Heterogeneous Graph Alignment for Joint Reasoning and Interpretabilitycited by 0
  2. 2026Unified Graph Prompt Learning via Low-Rank Graph Message Promptingcited by 0
  3. 2026What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Modelscited by 0