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Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal et al. , “Language Models Are Few-Shot Learners,” in NeurIPS , vol. 33, 2020, pp. 1877–1901
1901
Earlier work this paper cites.
M. T. Rosenstein, Z. Marx, L. P. Kaelbling, and T. G. Dietterich, “To transfer or not to transfer,” in NeurIPS , vol. 898, 2005
2005
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in KDD , 2014, pp. 701–710
2014
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering,” in NeurIPS , vol. 29, 2016
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in KDD , 2016, pp. 855–864
2016
Earlier work this paper cites.
M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, “Asymmetric Transitivity Preserving Graph Embedding,” in KDD , 2016, pp. 1105–1114
2016
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,” in ICML , 2017
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive Representation Learning on Large Graphs,” in NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
C. Wang, S. Pan, G. Long, X. Zhu, and J. Jiang, “MGAE: Marginalized Graph Autoencoder for Graph Clustering,” in CIKM , 2017, pp. 889–898
2017
Earlier work this paper cites.
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang, “Adversarially Regularized Graph Autoencoder for Graph Embedding,” in IJCAI , 2018, pp. 2609–2615
2018
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph Attention Networks,” in ICLR , 2018
2018
Earlier work this paper cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How Powerful are Graph Neural Networks?” in ICLR , 2018
2018
Earlier work this paper cites.
M. Zhang and Y. Chen, “Link Prediction Based on Graph Neural Networks,” in NeurIPS , 2018
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv preprint , 2019
2019
Earlier work this paper cites.
A. Hasanzadeh, E. Hajiramezanali, K. Narayanan, N. Duffield, M. Zhou, and X. Qian, “Semi-Implicit Graph Variational Auto-Encoders,” in NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
J. Park, M. Lee, H. J. Chang, K. Lee, and J. Y. Choi, “Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning,” in ICCV , 2019, pp. 6519–6528
2019
Earlier work this paper cites.
P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep Graph Infomax,” in ICLR , 2019
2019
Earlier work this paper cites.
X. Wang, Y. Zhang, and C. Shi, “Hyperbolic heterogeneous information network embedding,” in AAAI , vol. 33, 2019, pp. 5337–5344
2019
Earlier work this paper cites.
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding, “ProNE: Fast and Scalable Network Representation Learning,” in IJCAI , 2019, pp. 4278–4284
2019
Earlier work this paper cites.
K. Hassani and A. H. K. Ahmadi, “Contrastive Multi-View Representation Learning on Graphs,” in ICML , vol. 119, 2020, pp. 4116–4126
2020
Earlier work this paper cites.
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. S. Pande, and J. Leskovec, “Strategies for Pre-training Graph Neural Networks,” in ICLR , 2020
2020
Earlier work this paper cites.
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun, “GPT-GNN: Generative Pre-Training of Graph Neural Networks,” in KDD , 2020, pp. 1857–1867
2020
Earlier work this paper cites.
Z. Jiang, F. F. Xu, J. Araki, and G. Neubig, “How Can We Know What Language Models Know?” TACL , vol. 8, pp. 423–438, 2020
2020
Earlier work this paper cites.
Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang, “Graph Representation Learning via Graphical Mutual Information Maximization,” in The Web Conference , 2020, pp. 259–270
2020
Earlier work this paper cites.
J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang, “GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training,” in KDD , 2020, pp. 1150–1160
2020
Earlier work this paper cites.
Y. Rong, Y. Bian, T. Xu, W. Xie, Y. WEI, W. Huang, and J. Huang, “Self-Supervised Graph Transformer on Large-Scale Molecular Data,” in NeurIPS , vol. 33, 2020, pp. 12 559–12 571
2020
Earlier work this paper cites.
T. Shin, Y. Razeghi, R. L. L. IV, E. Wallace, and S. Singh, “Autoprompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts,” in EMNLP , 2020, pp. 4222–4235
2020
Earlier work this paper cites.
F.-Y. Sun, J. Hoffmann, V. Verma, and J. Tang, “InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization,” in ICLR , 2020
2020
Earlier work this paper cites.
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” in NeurIPS , 2020, pp. 5812–5823
2020
Earlier work this paper cites.
J. Zhang, H. Zhang, C. Xia, and L. Sun, “Graph-Bert: Only Attention is Needed for Learning Graph Representations,” arXiv preprint , 2020
2020
Earlier work this paper cites.
E. Dai and S. Wang, “Towards Self-Explainable Graph Neural Network,” in CIKM , 2021, pp. 302–311
2021
Earlier work this paper cites.
T. Gao, A. Fisch, and D. Chen, “Making Pre-Trained Language Models Better Few-Shot Learners,” in ACL , 2021, pp. 3816–3830
2021
Earlier work this paper cites.
A. Haviv, J. Berant, and A. Globerson, “BERTese: Learning to Speak to BERT,” in EACL , 2021, pp. 3618–3623
2021
Earlier work this paper cites.
M. He, Z. Wei, z. Huang, and H. Xu, “BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation,” in NeurIPS , vol. 34, 2021, pp. 14 239–14 251
2021
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “LoRA: Low-Rank Adaptation of Large Language Models,” arXiv preprint , 2021
2021
Earlier work this paper cites.
X. Jiang, T. Jia, Y. Fang, C. Shi, Z. Lin, and H. Wang, “Pre-training on Large-Scale Heterogeneous Graph,” in KDD , 2021, pp. 756–766
2021
Earlier work this paper cites.
X. Jiang, Y. Lu, Y. Fang, and C. Shi, “Contrastive Pre-Training of GNNs on Heterogeneous Graphs,” in CIKM , 2021, pp. 803–812
2021
Earlier work this paper cites.
M. Jin, Y. Zheng, Y.-F. Li, C. Gong, C. Zhou, and S. Pan, “Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning,” in IJCAI , 2021, pp. 1477–1483
2021
Earlier work this paper cites.
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node Similarity Preserving Graph Convolutional Networks,” in WSDM , 2021, pp. 148–156
2021
Earlier work this paper cites.
D. Kim and A. Oh, “How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision,” in ICLR , 2021
2021
Earlier work this paper cites.
B. Lester, R. Al-Rfou, and N. Constant, “The Power of Scale for Parameter-Efficient Prompt Tuning,” in EMNLP , 2021, pp. 3045–3059
2021
Earlier work this paper cites.
X. L. Li and P. Liang, “Prefix-Tuning: Optimizing Continuous Prompts for Generation,” in ACL-IJCNLP , 2021, pp. 4582–4597
2021
Earlier work this paper cites.
G. Qin and J. Eisner, “Learning How to Ask: Querying LMs with Mixtures of Soft Prompts,” in NAACL-HLT , 2021, pp. 5203–5212
2021
Cited alongside, same era.
T. Schick and H. Schütze, “Few-Shot Text Generation with Natural Language Instructions,” in EMNLP , 2021, pp. 390–402
2021
Cited alongside, same era.
——, “It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners,” in NAACL-HLT , 2021, pp. 2339–2352
2021
Cited alongside, same era.
Y. Shi, Z. Huang, S. Feng, H. Zhong, W. Wang, and Y. Sun, “Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification,” in IJCAI , vol. 2, 2021, pp. 1548–1554
2021
Cited alongside, same era.
A. Subramonian, “MOTIF-Driven Contrastive Learning of Graph Representations,” in AAAI , vol. 35, 2021, pp. 15 980–15 981
2021
Cited alongside, same era.
Y. Li, Z. Li, P. Wang, J. Li, X. Sun, H. Cheng, and J. X. Yu, “A survey of graph meets large language model: Progress and future directions,” arXiv preprint , 2023
2023
Closest in time.
H. Liu, J. Feng, L. Kong, N. Liang, D. Tao, Y. Chen, and M. Zhang, “One for All: Towards Training One Graph Model for All Classification Tasks,” arXiv preprint , 2023
2023
Closest in time.
J. Liu, C. Yang, Z. Lu, J. Chen, Y. Li, M. Zhang, T. Bai, Y. Fang, L. Sun, P. S. Yu, and C. Shi, “Towards Graph Foundation Models: A Survey and Beyond,” arXiv preprint , 2023
2023
Closest in time.
P. Liu, Y. Ren, and Z. Ren, “GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text,” arXiv preprint , 2023
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 195:1–195:35, 2023
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M. Sun, J. Xing, H. Wang, B. Chen, and J. Zhou, “MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph,” in KDD , 2021, pp. 3585–3594
2021
Cited alongside, same era.
X. Sun, H. Yin, B. Liu, H. Chen, J. Cao, Y. Shao, and N. Q. Viet Hung, “Heterogeneous Hypergraph Embedding for Graph Classification,” in WSDM , 2021, pp. 725–733
2021
Cited alongside, same era.
S. Suresh, P. Li, C. Hao, and J. Neville, “Adversarial Graph Augmentation to Improve Graph Contrastive Learning,” in NeurIPS , vol. 34, 2021, pp. 15 920–15 933
2021
Cited alongside, same era.
S. Thakoor, C. Tallec, M. G. Azar, R. Munos, P. Veličković, and M. Valko, “Bootstrapped representation learning on graphs,” in ICLR , 2021
2021
Cited alongside, same era.
M. Tsimpoukelli, J. L. Menick, S. Cabi, S. M. A. Eslami, O. Vinyals, and F. Hill, “Multimodal Few-Shot Learning with Frozen Language Models,” in NeurIPS , vol. 34, 2021, pp. 200–212
2021
Cited alongside, same era.
P. Wang, K. Agarwal, C. Ham, S. Choudhury, and C. K. Reddy, “Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks,” in The Web Conference , 2021, pp. 2946–2957
2021
Cited alongside, same era.
X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning,” in KDD , 2021, pp. 1726–1736
2021
Cited alongside, same era.
2023
Closest in time.
Z. Liu, X. Yu, Y. Fang, and X. Zhang, “Graphprompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks,” in The Web Conference , 2023, pp. 417–428
2023
Closest in time.
Z. Liu, S. Li, Y. Luo, H. Fei, Y. Cao, K. Kawaguchi, X. Wang, and T.-S. Chua, “MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter,” in EMNLP , 2023
2023
Closest in time.
Y. Ma, N. Yan, J. Li, M. Mortazavi, and N. V. Chawla, “HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks,” arXiv preprint , 2023
2023
Closest in time.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning Convolutional Neural Networks for Graphs,” in ICML , 2016, pp. 2014–2023
2023
Closest in time.
S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu, “Unifying Large Language Models and Knowledge Graphs: A Roadmap,” arXiv preprint , 2023
2023
Closest in time.
J. Park, A. Patel, O. Z. Khan, H. J. Kim, and J.-K. Kim, “Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models,” arXiv preprint , 2023
2023
Closest in time.
C. Qian, H. Tang, Z. Yang, H. Liang, and Y. Liu, “Can Large Language Models Empower Molecular Property Prediction?” arXiv preprint , 2023
2023
Closest in time.
J. Robinson, C. M. Rytting, and D. Wingate, “Leveraging Large Language Models for Multiple Choice Question Answering,” arXiv preprint , 2023
2023
Closest in time.
R. Shirkavand and H. Huang, “Deep Prompt Tuning for Graph Transformers,” arXiv preprint , 2023
2023
Closest in time.
X. Sun, H. Cheng, J. Li, B. Liu, and J. Guan, “All in One: Multi-Task Prompting for Graph Neural Networks,” in KDD , 2023, pp. 2120–2131
2023
Closest in time.
Q. Tan, N. Liu, X. Huang, S.-H. Choi, L. Li, R. Chen, and X. Hu, “S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking,” in WSDM , 2023, pp. 787–795
2023
Closest in time.
Z. Tan, R. Guo, K. Ding, and H. Liu, “Virtual Node Tuning for Few-shot Node Classification,” in KDD , 2023, pp. 2177–2188
2023
Closest in time.
Y. Tian, H. Song, Z. Wang, H. Wang, Z. Hu, F. Wang, N. V. Chawla, and P. Xu, “Graph Neural Prompting with Large Language Models,” arXiv preprint , 2023
2023
Closest in time.
H. Wang, T. Fu, Y. Du, W. Gao, K. Huang, Z. Liu et al. , “Scientific discovery in the age of artificial intelligence,” Nature , vol. 620, no. 7972, pp. 47–60, 2023
2023
Closest in time.
J. Wang, D. Chen, C. Luo, X. Dai, L. Yuan, Z. Wu, and Y.-G. Jiang, “ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System,” arXiv preprint , 2023
2023
Closest in time.
Y. Wang, N. Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr, “Knowledge Graph Prompting for Multi-Document Question Answering,” arXiv preprint , 2023
2023
Closest in time.
Z. Wen and Y. Fang, “Augmenting Low-Resource Text Classification with Graph-Grounded Pre-Training and Prompting,” in SIGIR , 2023, pp. 506–516
2023
Closest in time.
——, “Prompt Tuning on Graph-augmented Low-resource Text Classification,” arXiv preprint , 2023
2023
Closest in time.
Z. Wen, Y. Fang, Y. Liu, Y. Guo, and S. Hao, “Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks,” arXiv preprint , 2023
2023
Closest in time.
J. Wu, S. Li, A. Deng, M. Xiong, and H. Bryan, “Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection,” in CIKM , 2023
2023
Closest in time.
X. Wu, K. Zhou, M. Sun, X. Wang, and N. Liu, “A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges,” arXiv preprint , 2023
2023
Closest in time.
Y. Wu, R. Xie, Y. Zhu, F. Zhuang, X. Zhang, L. Lin, and Q. He, “Personalized Prompt for Sequential Recommendation,” arXiv preprint , 2023
2023
Closest in time.
Y. Xie, Z. Xu, J. Zhang, Z. Wang, and S. Ji, “Self-Supervised Learning of Graph Neural Networks: A Unified Review,” TPAML , vol. 45, no. 2, pp. 2412–2429, 2023
2023
Closest in time.
C. Yang, D. Bo, J. Liu, Y. Peng, B. Chen, H. Dai et al. , “Data-centric graph learning: A survey,” arXiv preprint , 2023
2023
Closest in time.
H. Yang, X. Zhao, Y. Li, H. Chen, and G. Xu, “An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations,” in NeurIPS , 2023
2023
Closest in time.
Z. Yi, I. Ounis, and C. Macdonald, “Contrastive Graph Prompt-tuning for Cross-domain Recommendation,” TOIS , 2023
2023
Closest in time.
J. Yu, H. Yin, X. Xia, T. Chen, J. Li, and Z. Huang, “Self-Supervised Learning for Recommender Systems: A Survey,” TKDE , pp. 1–20, 2023
2023
Closest in time.
H. Zhang, X. Li, and L. Bing, “Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding,” arXiv preprint , 2023
2023
Closest in time.
T. Zhang, F. Ladhak, E. Durmus, P. Liang, K. McKeown, and T. B. Hashimoto, “Benchmarking Large Language Models for News Summarization,” arXiv preprint , 2023
2023
Closest in time.
W. Zhang, Y. Zhu, M. Chen, Y. Geng, Y. Huang, Y. Xu, W. Song, and H. Chen, “Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer,” in The Web Conference , 2023, pp. 2581–2590
2023
Closest in time.
Z. Zhang, H. Li, Z. Zhang, Y. Qin, X. Wang, and W. Zhu, “Large graph models: A perspective,” arXiv preprint , 2023
2023
Closest in time.
H. Zhao, S. Liu, C. Ma, H. Xu, J. Fu, Z.-H. Deng, L. Kong, and Q. Liu, “GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning,” arXiv preprint , 2023
2023
Closest in time.
W. Zhao, Q. Wu, C. Yang, and J. Yan, “GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks,” in KDD , 2023, pp. 3525–3536
2023
Closest in time.
Y. Zhu, J. Guo, and S. Tang, “SGL-PT: A Strong Graph Learner with Graph Prompt Tuning,” arXiv preprint , 2023
2023
Closest in time.
B. Hao, C. Yang, L. Guo, J. Yu, and H. Yin, “Motif-Based Prompt Learning for Universal Cross-Domain Recommendation,” in WSDM , 2024
2024
Closest in time.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph Contrastive Learning with Adaptive Augmentation,” in The Web Conference , 2021, pp. 2069–2080
2080
Closest in time.