This repository contains a curated list of papers on (or related to) pre-training for graph neural networks (Pre-train4GNN), which are categorized based on their published years, graph types, pre-training strategies, tuning strategies, and applications.
Continuously updating!
Paper Title | Venue | Graph Type | Pre-training Strategy | Tuning Strategy | Application | Paper Link | Code Link |
---|---|---|---|---|---|---|---|
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning | NeurIPS 2024 | Multiplex | Contrastive | N/A | General | [PDF] | [Code] |
GraphPro: Graph Pre-training and Prompt Learning for Recommendation | WWW 2024 | Dynamic | Generative | Prompt | Recommendation | [PDF] | [Code] |
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery | AAAI 2024 | Static | MTL | N/A | Biology | [PDF] | [Code] |
Unified Pretraining for Recommendation via Task Hypergraphs | WSDM 2024 | Hypergraph | Generative | N/A | Recommendation | [PDF] | [Code] |
GraphFM: A Scalable Framework for Multi-Graph Pretraining | Arxiv 2024 | Multi-graphs | MTL | N/A | General | [PDF] | N/A |
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training | CIKM 2023 | KG | MTL | N/A | Recommendation | [PDF] | N/A |
Self-Contrastive Graph Diffusion Network | MM 2023 | Static | Contrastive | N/A | General | [PDF] | N/A |
All in One: Multi-Task Prompting for Graph Neural Networks | KDD 2023 | Static | Generative | Prompt | General | [PDF] | [Code] |
Automated 3D Pre-Training for Molecular Property Prediction | KDD 2023 | Static | Generative | N/A | Chemistry&Biology | [PDF] | N/A |
What’s Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders | KDD 2023 | Static | Generative | N/A | General | [PDF] | [Code] |
When to Pre-Train Graph Neural Networks? From Data Generation Perspective! | KDD 2023 | Static | Generative (Quantification) | N/A | General | [PDF] | [Code] |
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks | WWW 2023 | Static | Contrastive | Prompt | General | [PDF] | [Code] |
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner | WWW 2023 | Static | Generative | N/A | General | [PDF] | [Code] |
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer | WWW 2023 | KG | Generative | Prompt | General | [PDF] | [Code] |
Protein Representation Learning by Geometric Structure Pretraining | ICLR 2023 | Static | Contrastive | N/A | Biology | [PDF] | [Code] |
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting | SIGIR 2023 | Text Graph | Contrastive | Prompt | Text Classification | [PDF] | [Code] |
Self-Supervised Graph Structure Refinement for Graph Neural Networks | WSDM 2023 | Static | Contrastive | GSL | General | [PDF] | [Code] |
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation | RecSys 2023 | Static | MTL | N/A | Recommendation | [PDF] | [Code] |
MVRACE: Multi-view Graph Contrastive Encoding for Graph Neural Network Pre-training | CogSci 2023 | Static | Contrastive | N/A | General | [PDF] | N/A |
Curriculum Pre-training Heterogeneous Subgraph Transformer for Top-N Recommendation | TOIS 2023 | Heterogeneous | Curriculum | N/A | Recommendation | [PDF] | N/A |
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks | KDD 2022 | Static | Generative | Prompt | General | [PDF] | [Code] |
GraphMAE: Self-Supervised Masked Graph Autoencoders | KDD 2022 | Static | Generative | N/A | General | [PDF] | [Code] |
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries | KDD 2022 | KG | Generative | Generative | General | [PDF] | [Code] |
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction | KDD 2022 | GT | Generative | N/A | Biology | [PDF] | [Code] |
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting | KDD 2022 | ST-Graph | Generative | N/A | Time Series Forecasting | [PDF] | [Code] |
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering | NIPS 2022 | Heterogeneous | Clustering | N/A | General | [PDF] | [Code] |
Does GNN Pretraining Help Molecular Representation? | NIPS 2022 | Molecular | Evaluation | N/A | General | [PDF] | N/A |
Pre-training Molecular Graph Representation with 3D Geometry | ICLR 2022 | Static | Generative+Contrastive | N/A | Biology | [PDF] | [Code] |
Graph Pre-training for AMR Parsing and Generation | ACL 2022 | Semantic Graph | Generative | N/A | NLP (AMR) | [PDF] | [Code] |
Neural Graph Matching for Pre-training Graph Neural Networks | SDM 2022 | Static | Contrastive | N/A | General | [PDF] | [Code] |
Pre-train Graph Neural Networks for Brain Network Analysis | Big Data 2022 | Static | Contrastive | N/A | Biology | [PDF] | N/A |
GCCAD: Graph Contrastive Learning for Anomaly Detection | TKDE 2022 | Static | Contrastive | N/A | Anomaly Detection | [PDF] | [Code] |
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering | TOIS 2022 | Static | Contrastive | Meta-Learning | Recommendation | [PDF] | N/A |
Pre-training Graph Neural Networks for Link Prediction in Biomedical Networks | Bioinformatics 2022 | Static | Generative | N/A | Biology | [PDF] | [Code] |
Pre-Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding | Advanced Science 2022 | Dynamic | Generative | N/A | Biology | [PDF] | [Code] |
Pre-training on Dynamic Graph Neural Networks | Neurocomputing 2022 | Dynamic | Generative | N/A | General | [PDF] | [Code] |
Adaptive Transfer Learning on Graph Neural Networks | KDD 2021 | Static | N/A | Meta Learning | General | [PDF] | [Code] |
Pre-training on Large-Scale Heterogeneous Graph | KDD 2021 | Heterogeneous | Contrastive | N/A | General | [PDF] | [Code] |
Self-supervised Graph-level Representation Learning with Local and Global Structure | ICML 2021 | Static | Contrastive | N/A | Biology | [PDF] | [Code] |
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks | IJCAI 2021 | Static | Decomposition | N/A | General | [PDF] | N/A |
Contrastive Pre-Training of GNNs on Heterogeneous Graphs | CIKM 2021 | Heterogeneous | Contrastive | N/A | General | [PDF] | [Code] |
Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation | WSDM 2021 | Static | Generative+RL | N/A | Recommendation | [PDF] | [Code] |
Pre-training Graph Transformer with Multimodal Side Information for Recommendation | MM 2021 | Static | Generative | N/A | Recommendation | [PDF] | [Code] |
Learning to Pre-train Graph Neural Networks | AAAI 2021 | Static | Contrastive | N/A | General | [PDF] | [Code] |
GPT-GNN: Generative Pre-Training of Graph Neural Networks | KDD 2020 | Static | Generative | N/A | General | [PDF] | [Code] |
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training | KDD 2020 | Static | Contrastive | N/A | General | [PDF] | [Code] |
Strategies for Pre-training Graph Neural Networks | ICLR 2020 | Static | Generative | N/A | Chemistry&Biology | [PDF] | [Code] |
Pre-training of Graph Augmented Transformers for Medication Recommendation | IJCAI 2019 | Ontology Tree | Generative | N/A | Recommendation | [PDF] | [Code] |
Paper Title | Venue | Graph Type | Pre-training Strategy | Tuning Strategy | Application | PDF Link | Code Link |
---|---|---|---|---|---|---|---|
GraphGPT: Graph Learning with Generative Pre-trained Transformers | Arxiv 2023 | Static | Generative | N/A | General | [PDF] | [Code] |
GraphGPT: Graph Instruction Tuning for Large Language Models | Arxiv 2023 | Static | N/A | Prompt | General | [PDF] | [Code] |
AdapterGNN: Efficient Delta Tuning Improves Generalization Ability in Graph Neural Networks | Arxiv 2023 | Static | N/A | Adapter | General | [PDF] | N/A |
Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks | Arxiv 2023 | Static | N/A | AutoML | General | [PDF] | N/A |
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning | Arxiv 2023 | Static | Contrastive | Prompt | General | [PDF] | N/A |
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks | Arxiv 2023 | Dynamic | Contrastive | Enhanced Embedding | General | [PDF] | N/A |
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training | Arxiv 2023 | KG | MTL | N/A | Recommendation | [PDF] | N/A |
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning | Arxiv 2023 | Static | Generative | Prompt | General | [PDF] | N/A |
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training | Arxiv 2022 | Static | Contrastive | N/A | General | [PDF] | N/A |
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks | Arxiv 2022 | Static | Generative | N/A | General | [PDF] | N/A |
Hypergraph Pre-training with Graph Neural Networks | Arxiv 2021 | Hypergraph | Contrastive | N/A | General | [PDF] | N/A |
GRAPH-BERT: Only Attention is Needed for Learning Graph Representations | Arxiv 2020 | Static | Generative | N/A | General | [PDF] | [Code] |
Pre-training Graph Neural Networks with Kernels | Arxiv 2018 | Static | Kernel | N/A | General | [PDF] | N/A |
Paper Title | Venue | PDF Link | Code Link |
---|---|---|---|
Graph Prompt Learning: A Comprehensive Survey and Beyond | Arxiv 2023 | [PDF] | [Code] |
Towards Graph Foundation Models: A Survey and Beyond | Arxiv 2023 | [PDF] | N/A |
Generative Diffusion Models on Graphs: Methods and Applications | IJCAI 2023 | [PDF] | N/A |
Graph Meets LLMs: Towards Large Graph Models | Arxiv 2023 | [PDF] | [Code] |
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges | Arxiv 2023 | [PDF] | N/A |
A Survey of Pre-training on Graphs: Taxonomy, Methods and Applications | Arxiv 2022 | [PDF] | [Code] |
Self-Supervised Learning of Graph Neural Networks: A Unified Review | TPAMI 2022 | [PDF] | N/A |
Graph Self-Supervised Learning: A Survey | TKDE 2022 | [PDF] | N/A |
Self-supervised Learning on Graphs: Contrastive, Generative, or Predictive | TKDE 2021 | [PDF] | N/A |
Self-supervised Learning on Graphs: Deep Insights and New Directions | Arxiv 2020 | [PDF] | [Code] |
Tutorial Title | Venue | PDF Link |
---|---|---|
Self-supervised Learning and Pre-training on Graphs | WWW 2023 | [PDF] |