2021

Self-Supervised Learning of Graph Neural Networks: A Unified Review

Xie, Yaochen, Xu, Zhao, Zhang, Jingtun et al.

Understand

Deep models trained in supervised mode have achieved remarkable success on a variety of tasks.

When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples.
SSL has achieved promising performance on natural language and image learning tasks.
Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs).

Built on

M. D. Donsker and S. R. S. Varadhan, “Asymptotic evaluation of certain markov process expectations for large time. iv,” Communications on Pure and Applied Mathematics , vol. 36, pp. 183–212, 1983
1983
Earlier work this paper cites.
M. A. Kramer, “Nonlinear principal component analysis using autoassociative neural networks,” AIChE journal , vol. 37, no. 2, pp. 233–243, 1991
1991
Earlier work this paper cites.
A. K. Debnath, R. L. Lopez de Compadre, G. Debnath, A. J. Shusterman, and C. Hansch, “Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds correlation with molecular orbital energies and hydrophobicity,” Journal of Medicinal Chemistry , vol. 34, no. 2, pp. 786–797, 1991
1991
Earlier work this paper cites.
H. Hotelling, “Relations between two sets of variates,” in Breakthroughs in statistics . Springer, 1992, pp. 162–190
1992
Earlier work this paper cites.
G. Taubin, “A signal processing approach to fair surface design,” in Proceedings of the 22nd annual conference on Computer graphics and interactive techniques , 1995, pp. 351–358
1995
Earlier work this paper cites.
C. L. Giles, K. D. Bollacker, and S. Lawrence, “Citeseer: An automatic citation indexing system,” in Proceedings of the Third ACM Conference on Digital Libraries . Association for Computing Machinery, 1998, p. 89–98
1998
Earlier work this paper cites.
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” in Proceedings of the 37-th Annual Allerton Conference on Communication, Control and Computing , 1999, pp. 368–377
1999
Earlier work this paper cites.
A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore, “Automating the construction of internet portals with machine learning,” Information Retrieval , vol. 3, no. 2, pp. 127–163, 2000
2000
Earlier work this paper cites.
P. D. Dobson and A. J. Doig, “Distinguishing enzyme structures from non-enzymes without alignments,” Journal of Molecular Biology , vol. 330, no. 4, pp. 771–783, 2003
2003
Earlier work this paper cites.
D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical correlation analysis: An overview with application to learning methods,” Neural computation , vol. 16, no. 12, pp. 2639–2664, 2004
2004
Earlier work this paper cites.
K. M. Borgwardt, C. S. Ong, S. Schönauer, S. V. N. Vishwanathan, A. J. Smola, and H.-P. Kriegel, “Protein function prediction via graph kernels,” Bioinformatics , vol. 21, pp. i47–i56, 2005
2005
Earlier work this paper cites.
N. Wale and G. Karypis, “Comparison of descriptor spaces for chemical compound retrieval and classification,” in Sixth International Conference on Data Mining , 2006, pp. 678–689
2006
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008
2008
Earlier work this paper cites.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence , 2009, p. 452–461
2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics , 2010, pp. 297–304
2010
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.” Journal of Machine Learning Research , vol. 11, no. 12, 2010
2010
Earlier work this paper cites.
D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, “Why does unsupervised pre-training help deep learning?” Journal of Machine Learning Research , vol. 11, no. 19, pp. 625–660, 2010
2010
Earlier work this paper cites.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st International Conference on Learning Representations, Workshop Track Proceedings , 2013
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2014, pp. 701–710
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , vol. 27, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1422–1430
2015
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in International Conference on Machine Learning . PMLR, 2015, pp. 1180–1189
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 815–823
2015
Earlier work this paper cites.
E. Hoffer and N. Ailon, “Deep metric learning using triplet network,” in International workshop on similarity-based pattern recognition . Springer, 2015, pp. 84–92
2015
Earlier work this paper cites.
T. Sterling and J. J. Irwin, “Zinc 15–ligand discovery for everyone,” Journal of Chemical Information and Modeling , vol. 55, no. 11, pp. 2324–2337, 2015
2015
Earlier work this paper cites.
P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2015, p. 1365–1374
2015
Earlier work this paper cites.
A. Sinha, Z. Shen, Y. Song, H. Ma, D. Eide, B.-J. Hsu, and K. Wang, “An overview of microsoft academic service (MAS) and applications,” in Proceedings of the 24th international conference on world wide web , 2015, pp. 243–246
2015
Earlier work this paper cites.
J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel, “Image-based recommendations on styles and substitutes,” in Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval , 2015, pp. 43–52
2015
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in European conference on computer vision . Springer, 2016, pp. 69–84
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
Original
2016
Earlier work this paper cites.
S. Nowozin, B. Cseke, and R. Tomioka, “f-gan: Training generative neural samplers using variational divergence minimization,” in Advances in Neural Information Processing Systems , 2016, pp. 271–279
2016
Earlier work this paper cites.
K. Sohn, “Improved deep metric learning with multi-class n-pair loss objective,” in Advances in Neural Information Processing Systems , vol. 29, 2016, pp. 1857–1865
2016
Earlier work this paper cites.
Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in International conference on machine learning . PMLR, 2016, pp. 40–48
2016
Earlier work this paper cites.
A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and S. Jaiswal, “graph2vec: Learning distributed representations of graphs,” arXiv preprint arXiv:1707.05005 , 2017
Original
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 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 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management , 2017, pp. 889–898
2017
Earlier work this paper cites.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
M. Zitnik and J. Leskovec, “Predicting multicellular function through multi-layer tissue networks,” Bioinformatics , vol. 33, no. 14, pp. i190–i198, 2017
2017
Earlier work this paper cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Earlier work this paper cites.
A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018
Original
2018
Earlier work this paper cites.
A. Tsitsulin, D. Mottin, P. Karras, A. Bronstein, and E. Müller, “SGR: Self-supervised spectral graph representation learning,” arXiv preprint arXiv:1811.06237 , 2018
Original
2018
Earlier work this paper cites.
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande, “Moleculenet: a benchmark for molecular machine learning,” Chemical Science , vol. 9, no. 2, pp. 513–530, 2018
2018
Earlier work this paper cites.
H. Gao, Z. Wang, and S. Ji, “Large-scale learnable graph convolutional networks,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2018, pp. 1416–1424
2018
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 Proceedings of the 27th International Joint Conference on Artificial Intelligence , 2018, pp. 2609–2615
2018
Earlier work this paper cites.
M. I. Belghazi, A. Baratin, S. Rajeshwar, S. Ozair, Y. Bengio, A. Courville, and D. Hjelm, “Mutual information neural estimation,” in Proceedings of the 35th International Conference on Machine Learning , vol. 80. PMLR, 2018, pp. 531–540
2018
Earlier work this paper cites.

Similar

W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” in International Conference on Machine Learning . PMLR, 2018, pp. 2323–2332
2018
Cited alongside, same era.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Cited alongside, same era.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 132–149
2018
Cited alongside, same era.
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” arXiv preprint arXiv:1811.05868 , 2018
Original
2018
Cited alongside, same era.
J. You, R. Ying, X. Ren, W. Hamilton, and J. Leskovec, “GraphRNN: Generating realistic graphs with deep auto-regressive models,” in Proceedings of the 35th International Conference on Machine Learning , vol. 80. PMLR, 2018, pp. 5708–5717
2018
Cited alongside, same era.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. v. d. Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European semantic web conference . Springer, 2018, pp. 593–607
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
K. Xu, C. Li, Y. Tian, T. Sonobe, K.-i. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in International Conference on Machine Learning . PMLR, 2018, pp. 5453–5462
2018
Cited alongside, same era.
S. Laine, T. Karras, J. Lehtinen, and T. Aila, “High-quality self-supervised deep image denoising,” Advances in Neural Information Processing Systems , vol. 32, pp. 6970–6980, 2019
2019
Cited alongside, same era.
A. Krull, T.-O. Buchholz, and F. Jug, “Noise2Void-learning denoising from single noisy images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2129–2137
2019
Cited alongside, same era.
J. Batson and L. Royer, “Noise2Self: Blind denoising by self-supervision,” in Proceedings of the 36th International Conference on Machine Learning , vol. 97, 2019, pp. 524–533
2019
Cited alongside, same era.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2019, pp. 4171–4186
2019
Cited alongside, same era.

Then

W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec, “Strategies for pre-training graph neural networks,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
Y. Jiao, Y. Xiong, J. Zhang, Y. Zhang, T. Zhang, and Y. Zhu, “Sub-graph contrast for scalable self-supervised graph representation learning,” in IEEE International Conference on Data Mining . IEEE, 2020, pp. 222–231
2020
Later among the works it cites.
——, “Deep graph contrastive representation learning,” in ICML Workshop on Graph Representation Learning and Beyond , 2020
2020
Later among the works it 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 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1150–1160
2020
Later among the works it cites.
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 5812–5823
2020
Later among the works it cites.
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun, “GPT-GNN: Generative pre-training of graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1857–1867
2020
Later among the works it cites.
Z. Peng, Y. Dong, M. Luo, X.-M. Wu, and Q. Zheng, “Self-supervised graph representation learning via global context prediction,” arXiv preprint arXiv:2003.01604 , 2020
Original
2020
Later among the works it 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 Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
D. Hwang, J. Park, S. Kwon, K.-M. Kim, J.-W. Ha, and H. J. Kim, “Self-supervised auxiliary learning with meta-paths for heterogeneous graphs,” arXiv preprint arXiv:2007.08294 , 2020
Original
2020
Later among the works it cites.
K. Sun, Z. Lin, and Z. Zhu, “Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, 2020, pp. 5892–5899
2020
Later among the works it cites.
X. Liu, F. Zhang, Z. Hou, Z. Wang, L. Mian, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” arXiv preprint arXiv:2006.08218 , 2020
Original
2020
Later among the works it cites.
W. Jin, T. Derr, H. Liu, Y. Wang, S. Wang, Z. Liu, and J. Tang, “Self-supervised learning on graphs: Deep insights and new direction,” arXiv preprint arXiv:2006.10141 , 2020
Original
2020
Later among the works it cites.
P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola, “What makes for good views for contrastive learning?” arXiv preprint arXiv:2005.10243 , 2020
Original
2020
Later among the works it cites.
W. L. Hamilton, “Graph representation learning,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 14, no. 3, pp. 1–159, 2020
2020
Later among the works it cites.
Y. You, T. Chen, Z. Wang, and Y. Shen, “When does self-supervision help graph convolutional networks?” in International Conference on Machine Learning . PMLR, 2020, pp. 10 871–10 880
2020
Later among the works it cites.
J. Li, T. Y. J. Li, H. Zhang, K. Zhao, Y. Rong, and H. Cheng, “Dirichlet graph variational autoencoder,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
C. Morris, N. M. Kriege, F. Bause, K. Kersting, P. Mutzel, and M. Neumann, “Tudataset: A collection of benchmark datasets for learning with graphs,” in ICML 2020 Workshop on Graph Representation Learning and Beyond , 2020
2020
Later among the works it cites.
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna, “GraphSAINT: Graph sampling based inductive learning method,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
C. Shi*, M. Xu*, Z. Zhu, W. Zhang, M. Zhang, and J. Tang, “Graphaf: a flow-based autoregressive model for molecular graph generation,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
C. Zang and F. Wang, “Moflow: An invertible flow model for generating molecular graphs,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2020, p. 617–626
2020
Later among the works it cites.
H. Yuan, H. Yu, S. Gui, and S. Ji, “Explainability in graph neural networks: A taxonomic survey,” arXiv preprint arXiv:2012.15445 , 2020
Original
2020
Later among the works it cites.
M. Tschannen, J. Djolonga, P. K. Rubenstein, S. Gelly, and M. Lucic, “On mutual information maximization for representation learning,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
H. Gao, Y. Liu, and S. Ji, “Topology-aware graph pooling networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 12, pp. 4512–4518, 2021
2021
Closest in time.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in Proceedings of The Web Conference 2021 , 2021
2021
Closest in time.
S. Thakoor, C. Tallec, M. G. Azar, R. Munos, P. Veličković, and M. Valko, “Large-scale representation learning on graphs via bootstrapping,” arXiv preprint arXiv:2102.06514 , 2021
Original
2021
Closest in time.
Z. Hu, G. Kou, H. Zhang, N. Li, K. Yang, and L. Liu, “Rectifying pseudo labels: Iterative feature clustering for graph representation learning,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, p. 720–729
2021
Closest in time.
H. Zhang, Q. Wu, J. Yan, D. Wipf, and S. Y. Philip, “From canonical correlation analysis to self-supervised graph neural networks,” in Advances in Neural Information Processing Systems , 2021
2021
Closest in time.
Y. Liu, S. Pan, M. Jin, C. Zhou, F. Xia, and P. S. Yu, “Graph self-supervised learning: A survey,” arXiv preprint arXiv:2103.00111 , 2021
Original
2021
Closest in time.
Y. Wang, J. Zhang, S. Guo, H. Yin, C. Li, and H. Chen, “Decoupling representation learning and classification for gnn-based anomaly detection,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2021, p. 1239–1248
2021
Closest in time.
X. Jiang, T. Jia, Y. Fang, C. Shi, Z. Lin, and H. Wang, “Pre-training on large-scale heterogeneous graph,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2021, pp. 756––766
2021
Closest in time.
X. Wang, N. Liu, H. Han, and C. Shi, “Self-supervised heterogeneous graph neural network with co-contrastive learning,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2021, pp. 1726–1736
2021
Closest in time.
S. Suresh, P. Li, C. Hao, and J. Neville, “Adversarial graph augmentation to improve graph contrastive learning,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Closest in time.
D. Xu, W. Cheng, D. Luo, H. Chen, and X. Zhang, “Infogcl: Information-aware graph contrastive learning,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Closest in time.
C. Wei, K. Shen, Y. Chen, and T. Ma, “Theoretical analysis of self-training with deep networks on unlabeled data,” in International Conference on Learning Representations , 2021
2021
Closest in time.
D. Kim and A. Oh, “How to find your friendly neighborhood: Graph attention design with self-supervision,” in International Conference on Learning Representations , 2021
2021
Closest in time.
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node similarity preserving graph convolutional networks,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining . ACM, 2021
2021
Closest in time.
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny, “Barlow twins: Self-supervised learning via redundancy reduction,” in International Conference on Machine Learning . PMLR, 2021, pp. 12 310–12 320
2021
Closest in time.
M. Liu, Y. Luo, L. Wang, Y. Xie, H. Yuan, S. Gui, H. Yu, Z. Xu, J. Zhang, Y. Liu, K. Yan, H. Liu, C. Fu, B. M. Oztekin, X. Zhang, and S. Ji, “DIG: A turnkey library for diving into graph deep learning research,” Journal of Machine Learning Research , vol. 22, no. 240, pp. 1–9, 2021
2021
Closest in time.
Y. Luo, K. Yan, and S. Ji, “Graphdf: A discrete flow model for molecular graph generation,” in Proceedings of the 38th International Conference on Machine Learning , vol. 139. PMLR, 2021, pp. 7192–7203
2021
Closest in time.
P. Li, J. Wang, Z. Li, Y. Qiao, X. Liu, F. Ma, P. Gao, S. Song, and G. Xie, “Pairwise half-graph discrimination: A simple graph-level self-supervised strategy for pre-training graph neural networks,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence , 2021, pp. 2694–2700
2021
Closest in time.
S. Tian, R. Wu, L. Shi, L. Zhu, and T. Xiong, “Self-supervised representation learning on dynamic graphs,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 1814–1823
2021
Closest in time.
Y. Xie, Z. Xu, and S. Ji, “Self-supervised representation learning via latent graph prediction,” arXiv preprint arXiv:2202.08333 , 2022
Original
2022
Closest in time.
Y. Xie, S. Katariya, X. Tang, E. Huang, N. Rao, K. Subbian, and S. Ji, “Task-agnostic graph explanations,” arXiv preprint arXiv:2202.08335 , 2022
Original
2022
Closest in time.
H. Gao and S. Ji, “Graph U-Nets,” in Proceedings of the 36th International Conference on Machine Learning , 2019, pp. 2083–2092
2092
Closest in time.

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