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Graph-level representations are critical in various real-world applications, such as predicting the properties of molecules.
1904
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Sam T Roweis and Lawrence K Saul · 2000
Earlier work this paper cites.
Finding and evaluating community structure in networks
Mark EJ Newman and Michelle Girvan · 2004
Earlier work this paper cites.
Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel · 2005
Earlier work this paper cites.
Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
Earlier work this paper cites.
M. Gutmann and A. Hyvärinen, “Noise-contrastive estimation: A new estimation principle for unnormalized statistical models,” in AISTATS , 2010, pp. 297–304
2010
Earlier work this paper cites.
Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Earlier work this paper cites.
Libsvm: A library for support vector machines
Chih-Chung Chang and Chih-Jen Lin · 2011
Earlier work this paper cites.
A new protein graph model for function prediction
Marco A Alvarez and Changhui Yan · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Accelerating minibatch stochastic gradient descent using stratified sampling
Peilin Zhao and Tong Zhang · 2014
Earlier work this paper cites.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in ICCV , 2015, pp. 1026–1034
2015
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
The multiscale laplacian graph kernel
Risi Kondor and Horace Pan · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Earlier work this paper cites.
Aptrank: an adaptive pagerank model for protein function prediction on bi-relational graphs
Biaobin Jiang, Kyle Kloster, David F Gleich, and Michael Gribskov · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Information retrieval using a singular value decomposition model of latent semantic structure
George W Furnas, Scott Deerwester, Susan T Durnais, Thomas K Landauer, Richard A Harshman, Lynn A Streeter, and Karen E Lochbaum · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Earlier work this paper cites.
graph2vec: Learning distributed representations of graphs
Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal · 2017
Earlier work this paper cites.
Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in AAAI , 2018
2018
Earlier work this paper cites.
Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
Earlier work this paper cites.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
Earlier work this paper cites.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Z. Li, F. Nie, X. Chang, Y. Yang, C. Zhang, and N. Sebe, “Dynamic affinity graph construction for spectral clustering using multiple features,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, pp. 6323–6332, 2018
2018
Cited alongside, same era.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in ECCV
2018
Cited alongside, same era.
Z. Li, F. Nie, X. Chang, Y. Yang, C. Zhang, and N. Sebe, “Dynamic affinity graph construction for spectral clustering using multiple features,” IEEE transactions on neural networks and learning systems , vol. 29, no. 12, pp. 6323–6332, 2018
2018
Cited alongside, same era.
Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Y. Ren, B. Liu, C. Huang, P. Dai, L. Bo, and J. Zhang, “Heterogeneous deep graph infomax,” 2020
2020
Later among the works it cites.
Q. Zhu, B. Du, and P. Yan, “Self-supervised training of graph convolutional networks,” 2020
2020
Later among the works it cites.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Deep graph contrastive representation learning,” 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 ICDM , pages 222–231. IEEE, 2020
2020
Later among the works it cites.
D. Yuan, X. Chang, P.-Y. Huang, Q. Liu, and Z. He, “Self-supervised deep correlation tracking,” IEEE Transactions on Image Processing , vol. 30, pp. 976–985, 2020
2020
Later among the works it cites.
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Graph matching networks for learning the similarity of graph structured objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli · 2019
Cited alongside, same era.
Deep graph infomax
Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
Cited alongside, same era.
On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
Cited alongside, same era.
Unsupervised pre-training of image features on non-curated data
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2019
Cited alongside, same era.
Unsupervised deep learning by neighbourhood discovery
Jiabo Huang, Qi Dong, Shaogang Gong, and Xiatian Zhu · 2019
Cited alongside, same era.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
Cited alongside, same era.
A theoretical analysis of contrastive unsupervised representation learning
Sanjeev Arora, Hrishikesh Khandeparkar, Mikhail Khodak, Orestis Plevrakis, and Nikunj Saunshi · 2019
Cited alongside, same era.
J. Li, P. Zhou, C. Xiong, and S. Hoi, “Prototypical contrastive learning of unsupervised representations,” in ICLR , 2020
2020
Later among the works it cites.
B. Hui, P. Zhu, and Q. Hu, “Collaborative graph convolutional networks: Unsupervised learning meets semi-supervised learning,” in AAAI , vol. 34, no. 04, 2020, pp. 4215–4222
2020
Later among the works it cites.
B. Hui, P. Zhu, and Q. Hu, “Collaborative graph convolutional networks: Unsupervised learning meets semi-supervised learning,” in AAAI , 2020
2020
Later among the works it cites.
M. Sun, J. Xing, H. Wang, B. Chen, and J. Zhou, “Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge,” SIGKDD , 2021
2021
Closest in time.
Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Phillip S Yu, and Lifang He · 2021
Closest in time.
Y. You, T. Chen, Y. Shen, and Z. Wang, “Graph contrastive learning automated,” in ICML
2021
Closest in time.
H. Zhao, X. Yang, Z. Wang, E. Yang, and C. Deng, “Graph debiased contrastive learning with joint representation clustering,” in IJCAI , 2021, pp. 3434–3440
2021
Closest in time.
M. Xu, H. Wang, B. Ni, H. Guo, and J. Tang, “Self-supervised graph-level representation learning with local and global structure,” in ICML
2021
Closest in time.
Z. Tong, Y. Liang, H. Ding, Y. Dai, X. Li, and C. Wang, “Directed graph contrastive learning,” NeurIPS , vol. 34, 2021
2021
Closest in time.
L. Liu, Z. Kang, L. Tian, W. Xu, and X. He, “Multilayer graph contrastive clustering network,” 2021
2021
Closest in time.
E. Pan et al. , “Multi-view contrastive graph clustering,” in NeurIPS , 2021
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 CIKM , pages 720–729. ACM, 2021
2021
Closest in time.
W. Xia, Q. Gao, M. Yang, and X. Gao, “Self-supervised contrastive attributed graph clustering,” 2021
2021
Closest in time.
J. Zeng and P. Xie, “Contrastive self-supervised learning for graph classification,” in AAAI , pages 10824–10832, 2021
2021
Closest in time.
“Graph contrastive learning with adaptive augmentation”, WebConf , Apr 2021
2021
Closest in time.
L. Wu, H. Lin, C. Tan, Z. Gao, and S. Z. Li, “Self-supervised learning on graphs: Contrastive, generative, or predictive,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
Closest in time.
Y. Liu, M. Jin, S. Pan, C. Zhou, F. Xia, and P. S. Yu, “Graph self-supervised learning: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2021
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 SIGKDD , 2021, pp. 756–766
2021
Closest in time.
2021
Closest in time.
P. Zhou, C. Xiong, X. Yuan, and S. Hoi, “A theory-driven self-labeling refinement method for contrastive representation learning,” in NeurIPS , 2021
2021
Closest in time.
Y. Fang, Q. Zhang, H. Yang, X. Zhuang, S. Deng, W. Zhang, M. Qin, Z. Chen, X. Fan, and H. Chen, “Molecular contrastive learning with chemical element knowledge graph,” in AAAI , 2022
2022
Closest in time.
Y. Hou, B. Hu, W. X. Zhao, Z. Zhang, J. Zhou, and J.-R. Wen, “Neural graph matching for pre-training graph neural networks,” in SDM . SIAM, 2022, pp. 172–180
2022
Closest in time.
H. Hafidi, M. Ghogho, P. Ciblat, and A. Swami, “Negative sampling strategies for contrastive self-supervised learning of graph representations,” Signal Processing , vol. 190, p. 108310, 2022
2022
Closest in time.
N. Lee, J. Lee, and C. Park, “Augmentation-free self-supervised learning on graphs,” in AAAI , 2022
2022
Closest in time.
Z. Lin, C. Tian, Y. Hou, and W. X. Zhao, “Improving graph collaborative filtering with neighborhood-enriched contrastive learning,” in WebConf , 2022, pp. 2320–2329
2022
Closest in time.