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Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention.
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang. 2019 · 1904
Earlier work this paper cites.
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aäron van den Oord. 2019 · 1905
Earlier work this paper cites.
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019 · 1908
Earlier work this paper cites.
Design and selection of novel Cys2His2 zinc finger proteins
Carl O Pabo, Ezra Peisach, and Robert A Grant. 2001 · 2001
Earlier work this paper cites.
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2002
Earlier work this paper cites.
Network motifs: simple building blocks of complex networks
Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon. 2002 · 2002
Earlier work this paper cites.
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
Nadav Kashtan, Shalev Itzkovitz, Ron Milo, and Uri Alon. 2004 · 2004
Earlier work this paper cites.
Frequency concepts and pattern detection for the analysis of motifs in networks
Falk Schreiber and Henning Schwöbbermeyer. 2005 · 2005
Earlier work this paper cites.
Nemofinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining . 106–115
Jin Chen, Wynne Hsu, Mong Li Lee, and See-Kiong Ng. 2006 · 2006
Earlier work this paper cites.
RDKit: Open-source cheminformatics
Greg Landrum et al · 2006
Earlier work this paper cites.
DeeperGCN: All You Need to Train Deeper GCNs
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem. 2020 · 2006
Earlier work this paper cites.
Efficient detection of network motifs
Sebastian Wernicke. 2006 · 2006
Earlier work this paper cites.
Self-Supervised Graph Transformer on Large-Scale Molecular Data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020 · 2007
Cited alongside, same era.
Rolx: structural role extraction & mining in large graphs. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining . 1231–1239
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos, and Lei Li. 2012 · 2012
Cited alongside, same era.
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances
Marco Cuturi. 2013 · 2013
Cited alongside, same era.
Probabilistic latent semantic analysis
Thomas Hofmann. 2013 · 2013
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Later among the works it cites.
An end-to-end deep learning architecture for graph classification. In Thirty-Second AAAI Conference on Artificial Intelligence
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen. 2018 · 2018
Later among the works it cites.
Learning Representations by Maximizing Mutual Information Across Views. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, Emily B. Fox, and Roman Garnett (Eds.). 15509–15519
Philip Bachman, R. Devon Hjelm, and William Buchwalter. 2019 · 2019
Later among the works it cites.
Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2019 · 2019
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Deep graph kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1365–1374
Pinar Yanardag and SVN Vishwanathan. 2015 · 2015
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Inductive representation learning on large graphs. In Advances in neural information processing systems . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Neural discrete representation learning. In Advances in Neural Information Processing Systems . 6306–6315
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Pre-training Graph Neural Networks with Kernels
Nicolò Navarin, Dinh V. Tran, and Alessandro Sperduti. 2018 · 2018
Cited alongside, same era.
Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Cited alongside, same era.
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Learning deep representations by mutual information estimation and maximization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio. 2019 · 2019
Later among the works it cites.
Spectral clustering with graph neural networks for graph pooling. In International Conference on Machine Learning . PMLR, 874–883
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi. 2020 · 2020
Closest in time.
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020 · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020b · 2020
Closest in time.
Graph Contrastive Coding for Graph Neural Network Pre-Training. In KDD
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Closest in time.
Inductive and Unsupervised Representation Learning on Graph Structured Objects. In International Conference on Learning Representations
Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, and Yun Fu. 2020 · 2020
Closest in time.
Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations (ICLR)
Asano YM., Rupprecht C., and Vedaldi A. 2020 · 2020
Closest in time.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
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