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GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning

arXiv 2310.07365 ↗First posted 11 Oct 2023cs.LGcited by 32

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Abstract (arXiv)

Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on the Web offer immense potential for these applications. Graph self-supervised algorithms have achieved significant success in acquiring generic knowledge from abundant unlabeled graph data. These pre-trained models can be applied to various downstream Web applications, saving training time and improving downstream (target) performance. However, different graphs, even across seemingly similar domains, can differ significantly in terms of attribute semantics, posing difficulties, if not infeasibility, for transferring the pre-trained models to downstream tasks. Concretely speaking, for example, the additional task-specific node information in downstream tasks (specificity) is usually deliberately omitted so that the pre-trained representation (transferability) can be leveraged. The trade-off as such is termed as "transferability-specificity dilemma" in this work. To address this challenge, we introduce an innovative deployment module coined as GraphControl, motivated by ControlNet, to realize better graph domain transfer learning. Specifically, by leveraging universal structural pre-trained models and GraphControl, we align the input space across various graphs and incorporate unique characteristics of target data as conditional inputs. These conditions will be progressively integrated into the model during fine-tuning or prompt tuning through ControlNet, facilitating personalized deployment. Extensive experiments show that our method significantly enhances the adaptability of pre-trained models on target attributed datasets, achieving 1.4-3x performance gain. Furthermore, it outperforms training-from-scratch methods on target data with a comparable margin and exhibits faster convergence.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 41 of its 62 references that are in the map. First posted on arXiv 11 Oct 2023.

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41 of 62 references.

References · 41

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
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29 more
  1. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  2. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  3. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  4. 2015Image-based Recommendations on Styles and Substitutescited by 702
  5. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  6. 2020Deep Graph Contrastive Representation Learningcited by 535
  7. 2020GCC: Graph Contrastive Coding for Graph Neural Network Pre-Trainingcited by 461
  8. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  9. 2019InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximizationcited by 441
  10. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  11. 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 332
  12. 2017Few-Shot Learning with Graph Neural Networkscited by 324
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  18. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 17; 2025: 7; 2026: 6.

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  1. 2023Graph Foundation Models: Concepts, Opportunities and Challengescited by 101
  2. 2023Graph Prompt Learning: A Comprehensive Survey and Beyondcited by 61
  3. 2025SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training and Cross-domain Adaptationcited by 61
  4. 2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
  5. 2024Efficient Tuning and Inference for Large Language Models on Textual Graphscited by 54
  6. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 36
  7. 2024Towards Graph Contrastive Learning: A Survey and Beyondcited by 26
  8. 2024GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learningcited by 21
  9. 2024Towards Unified Multimodal Editing with Enhanced Knowledge Collaborationcited by 18
  10. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 16
  11. 2024Bridging Local Details and Global Context in Text-Attributed Graphscited by 15
  12. 2025GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuningcited by 13
20 more
  1. 2025DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approachcited by 11
  2. 2025One Prompt Fits All: Universal Graph Adaptation for Pretrained Modelscited by 11
  3. 2024Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysiscited by 10
  4. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  5. 2024AnyTrans: Translate AnyText in the Image with Large Scale Modelscited by 8
  6. 2024Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphscited by 6
  7. 2024Semantic Codebook Learning for Dynamic Recommendation Modelscited by 6
  8. 2024Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learningcited by 6
  9. 2025GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learningcited by 6
  10. 2024Towards Cross-domain Few-shot Graph Anomaly Detectioncited by 5
  11. 2026MUG: Meta-path-aware Universal Heterogeneous Graph Pre-Trainingcited by 4
  12. 2024UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Taskscited by 3
  13. 2025Learning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessmentcited by 3
  14. 2024Graph Triple Attention Network: A Decoupled Perspectivecited by 2
  15. 2025A Survey of Cross-domain Graph Learning: Progress and Future Directionscited by 2
  16. 2026LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-trainingcited by 2
  17. 2026KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generationcited by 1
  18. 2026Robust Graph Fine-Tuning with Adversarial Graph Promptingcited by 0
  19. 2026GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networkscited by 0
  20. 2026Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPLcited by 0