Graph Prompting for Graph Learning Models: Recent Advances and Future Directions | Paper Citation Record

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This paper cites ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt.

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt

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This paper cites MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning.

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning

Reference 7

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks

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This paper cites OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

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This paper cites Pre-Training Graph Neural Networks for Generic Structural Feature Extraction.

Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions A Unified Graph Selective Prompt Learning for Graph Neural Networks

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Self-supervised Learning on Graphs: Deep Insights and New Direction

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Self-supervised Graph Neural Networks without explicit negative sampling

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Towards Robust Graph Contrastive Learning

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Variational Graph Auto-Encoders

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions The Power of Scale for Parameter-Efficient Prompt Tuning

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Subgraph-level Universal Prompt Tuning

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Towards Graph Prompt Learning: A Survey and Beyond

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Pre-training Graph Neural Networks with Kernels

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Calibrating and Improving Graph Contrastive Learning

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Spatio-Temporal Deep Graph Infomax

Reference 74

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions arXiv preprint arXiv:2003.01604 (2020)

Reference 78

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Graph-level Representation Learning with Joint-Embedding Predictive Architectures

Reference 81

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Heterogeneous Deep Graph Infomax

Reference 84

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 86

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Unresolved cited work

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization

Reference 99

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Observation 3ea44f9b-f2eb-402c-a8f0-9ae2086bdd87 · outbound

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Graph Prompting for Graph Learning Models: Recent Advances and Future Directions Large-Scale Representation Learning on Graphs via Bootstrapping

Reference 100

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