Fetching the paper…
Reading the bibliography…
Graph neural networks have emerged as a powerful tool for graph representation learning, but their performance heavily relies on abundant task-specific supervision.
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels,” Bioinformatics , vol. 21, no. suppl_1, pp. i47–i56, 2005
2005
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels.” JMLR , vol. 12, no. 9, 2011
2011
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: Online learning of social representations,” in SIGKDD , 2014, pp. 701–710
2014
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “LINE: Large-scale information network embedding,” in WWW , 2015, pp. 1067–1077
2015
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” NeurIPS , vol. 28, 2015
2015
Earlier work this paper cites.
R. A. Rossi and N. K. Ahmed, “The network data repository with interactive graph analytics and visualization,” in AAAI , 2015, pp. 4292–4293
2015
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in SIGKDD , 2016, pp. 855–864
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” in Bayesian Deep Learning Workshop , 2016
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” NeurIPS , vol. 30, pp. 1025–1035, 2017
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in ICML , 2017, pp. 1263–1272
2017
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, pp. 1126–1135
2017
Earlier work this paper cites.
H. Cai, V. W. Zheng, and K. C.-C. Chang, “A comprehensive survey of graph embedding: Problems, techniques, and applications,” TKDE , vol. 30, no. 9, pp. 1616–1637, 2018
2018
Earlier work this paper cites.
M. Zhang and Y. Chen, “Link prediction based on graph neural networks,” NeurIPS , vol. 31, 2018
2018
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in ICLR , 2018
2018
Earlier work this paper cites.
M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in AAAI , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” NeurIPS , vol. 31, pp. 4805–4815, 2018
2018
Earlier work this paper cites.
L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.-W. Hon, “Unified language model pre-training for natural language understanding and generation,” NeurIPS , vol. 32, 2019
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.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in ICLR , 2019
2019
Earlier work this paper cites.
J. Lee, I. Lee, and J. Kang, “Self-attention graph pooling,” in ICML , 2019, pp. 3734–3743
2019
Earlier work this paper cites.
Y. Ma, S. Wang, C. C. Aggarwal, and J. Tang, “Graph convolutional networks with eigenpooling,” in SIGKDD , 2019, pp. 723–731
2019
Earlier work this paper cites.
I. Beltagy, K. Lo, and A. Cohan, “Scibert: A pretrained language model for scientific text,” in NAACL , 2019, pp. 3615–3620
2019
Earlier work this paper cites.
J. Lu, D. Batra, D. Parikh, and S. Lee, “ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,” NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
M. Togninalli, E. Ghisu, F. Llinares-López, B. Rieck, and K. Borgwardt, “Wasserstein weisfeiler-lehman graph kernels,” NeurIPS , vol. 32, 2019
2019
Earlier work this paper cites.
F. Zhou, C. Cao, K. Zhang, G. Trajcevski, T. Zhong, and J. Geng, “Meta-GNN: On few-shot node classification in graph meta-learning,” in CIKM , 2019, pp. 2357–2360
2019
Earlier work this paper cites.
N. Wang, M. Luo, K. Ding, L. Zhang, J. Li, and Q. Zheng, “Graph few-shot learning with attribute matching,” in CIKM , 2020, pp. 1545–1554
2019
Earlier work this paper cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” TNNLS , vol. 32, no. 1, pp. 4–24, 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 SIGKDD , 2020, pp. 1857–1867
2020
Earlier work this paper cites.
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec, “Strategies for pre-training graph neural networks,” in ICLR , 2020
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 SIGKDD , 2020, pp. 1150–1160
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” NeurIPS , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
K. Huang and M. Zitnik, “Graph meta learning via local subgraphs,” NeurIPS , vol. 33, pp. 5862–5874, 2020
2020
Cited alongside, same era.
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” NeurIPS , vol. 33, pp. 5812–5823, 2020
2020
Cited alongside, same era.
F.-Y. Sun, J. Hoffman, V. Verma, and J. Tang, “Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,” in ICLR , 2020
M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang, “Gppt: Graph pre-training and prompt tuning to generalize graph neural networks,” in SIGKDD , 2022, pp. 1717–1727
2022
Later among the works it cites.
S. Wang, Y. Dong, X. Huang, C. Chen, and J. Li, “FAITH: Few-shot graph classification with hierarchical task graphs,” in IJCAI , 2022
2022
Later among the works it cites.
K. Wang, Y. Liang, X. Li, G. Li, B. Ghanem, R. Zimmermann, H. Yi, Y. Zhang, Y. Wang et al. , “Brave the wind and the waves: Discovering robust and generalizable graph lottery tickets,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Z. Liu, W. Zhang, Y. Fang, X. Zhang, and S. C. H. Hoi, “Towards locality-aware meta-learning of tail node embeddings on networks,” in CIKM , 2020, pp. 975–984
2020
Cited alongside, same era.
D. Hwang, J. Park, S. Kwon, K. Kim, J.-W. Ha, and H. J. Kim, “Self-supervised auxiliary learning with meta-paths for heterogeneous graphs,” NeurIPS , vol. 33, pp. 10 294–10 305, 2020
2020
Cited alongside, same era.
A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A survey on contrastive self-supervised learning,” Technologies , vol. 9, no. 1, p. 2, 2020
2020
Cited alongside, same era.
Y. Lu, X. Jiang, Y. Fang, and C. Shi, “Learning to pre-train graph neural networks,” in AAAI , 2021, pp. 4276–4284
2021
Cited alongside, same era.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE TKDE , vol. 35, no. 1, pp. 857–876, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
D. Bo, X. Wang, C. Shi, and H. Shen, “Beyond low-frequency information in graph convolutional networks,” in AAAI , 2021
2021
Cited alongside, same era.
2023
Closest in time.
D. Zhang, W. Feng, Y. Wang, Z. Qi, Y. Shan, and J. Tang, “Dropconn: Dropout connection based random gnns for molecular property prediction,” IEEE TKDE , 2023
2023
Closest in time.
Y. Mo, Y. Chen, Y. Lei, L. Peng, X. Shi, C. Yuan, and X. Zhu, “Multiplex graph representation learning via dual correlation reduction,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
Y. Fang, Y. Qin, H. Luo, F. Zhao, and K. Zheng, “Stwave+: A multi-scale efficient spectral graph attention network with long-term trends for disentangled traffic flow forecasting,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
J. Fang, W. Liu, Y. Gao, Z. Liu, A. Zhang, X. Wang, and X. He, “Evaluating post-hoc explanations for graph neural networks via robustness analysis,” in NeurIPS , 2023
2023
Closest in time.
Y. Mo, Y. Lei, J. Shen, X. Shi, H. T. Shen, and X. Zhu, “Disentangled multiplex graph representation learning,” in ICML , 2023
2023
Closest in time.
Y. Fang, Y. Qin, H. Luo, F. Zhao, B. Xu, L. Zeng, and C. Wang, “When spatio-temporal meet wavelets: Disentangled traffic forecasting via efficient spectral graph attention networks,” in ICDE , 2023
2023
Closest in time.
L. Hu, Z. Liu, Z. Zhao, L. Hou, L. Nie, and J. Li, “A survey of knowledge enhanced pre-trained language models,” IEEE TKDE , 2023
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 WWW , 2023, pp. 417–428
2023
Closest in time.
X. Jiang, D. Zhuang, X. Zhang, H. Chen, J. Luo, and X. Gao, “Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction,” in CIKM , 2023
2023
Closest in time.
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 SIGKDD , 2023
2023
Closest in time.
2023
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Li, X. Wang, Y. Xing, S. Fan, R. Wang, Y. Liu, and C. Shi, “Graph fairness learning under distribution shifts,” in WWW , 2024
2024
Closest in time.
X. Jiang, Z. Qin, J. Xu, and X. Ao, “Incomplete graph learning via attribute-structure decoupled variational auto-encoder,” in WSDM , 2024
2024
Closest in time.
Y. Li, X. Wang, H. Liu, and C. Shi, “A generalized neural diffusion framework on graphs,” in AAAI , 2024
2024
Closest in time.
Y. Wu, Y. Fang, and L. Liao, “On the feasibility of simple transformer for dynamic graph modeling,” in WWW , 2024
2024
Closest in time.
2024
Closest in time.
X. Yu, Z. Liu, Y. Fang, and X. Zhang, “Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning,” in AAAI , 2024
2024
Closest in time.
2024
Closest in time.
X. Yu, C. Zhou, Y. Fang, and X. Zhang, “Multigprompt for multi-task pre-training and prompting on graphs,” in WWW , 2024
2024
Closest in time.
2024
Closest in time.
H. Gao and S. Ji, “Graph u-nets,” in ICML , 2019, pp. 2083–2092
2092
Closest in time.