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Node representation learning has demonstrated its effectiveness for various applications on graphs.
A. W. Marshall, I. Olkin, and B. C. Arnold, Inequalities: theory of majorization and its applications . Springer, 1979, vol. 143
1979
Earlier work this paper cites.
J. Lee Rodgers and W. A. Nicewander, “Thirteen ways to look at the correlation coefficient,” The American Statistician , vol. 42, no. 1, pp. 59–66, 1988
1988
Earlier work this paper cites.
D. Zwillinger and S. Kokoska, CRC standard probability and statistics tables and formulae . Crc Press, 1999
1999
Earlier work this paper cites.
2006
Earlier work this paper cites.
A. Hagberg, P. Swart, and D. S Chult, “Exploring network structure, dynamics, and function using networkx,” in Proc. Python in Science Conference (SciPy) , August 2008
2008
Earlier work this paper cites.
K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification.” Journal of Machine Learning Research (JMLR) , vol. 10, no. 2, 2009
2009
Earlier work this paper cites.
A. Mislove, B. Viswanath, K. P. Gummadi, and P. Druschel, “You are who you know: inferring user profiles in online social networks,” in Proc. ACM International Conference on Web Search and Data Mining (WSDM) , February 2010, pp. 251–260
2010
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proc. International Conference on Artificial Intelligence and Statistics (AISTATS) , May 2010, pp. 249–256
2010
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proc. Innovations in Theoretical Computer Science (ITCS) , January 2012, pp. 214–226
2012
Earlier work this paper cites.
L. Takac and M. Zabovsky, “Data analysis in public social networks,” in International Scientific Conference and International Workshop. ’Present Day Trends of Innovations’ , vol. 1, no. 6, May 2012
2012
Earlier work this paper cites.
S. Hajian and J. Domingo-Ferrer, “A methodology for direct and indirect discrimination prevention in data mining,” IEEE Transactions on Knowledge and Data Engineering , vol. 25, no. 7, pp. 1445–1459, July 2013
2013
Earlier work this paper cites.
A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A. J. Smola, “Distributed large-scale natural graph factorization,” in Proc. International Conference on World Wide Web (WWW) , May 2013, pp. 37–48
2013
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) , August 2014, pp. 701–710. [Online]. Available: http://doi.acm.org/10.1145/2623330.2623732
2014
Earlier work this paper cites.
G. Bianconi, R. K. Darst, J. Iacovacci, and S. Fortunato, “Triadic closure as a basic generating mechanism of communities in complex networks,” Physical Review E , vol. 90, no. 4, p. 042806, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Cao, W. Lu, and Q. Xu, “Grarep: Learning graph representations with global structural information,” in Proc. ACM International Conference on Information and Knowledge Management (CIKM) , October 2015, pp. 891–900
2015
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 Proc. International Conference on World Wide Web (WWW) , May 2015, pp. 1067–1077
2015
Earlier work this paper cites.
M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, “Asymmetric transitivity preserving graph embedding,” in Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) , August 2016, pp. 1105–1114
2016
Cited alongside, same era.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) , August 2016
2016
Cited alongside, same era.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. International Conference on Learning Representations (ICLR) , April 2017
2017
Cited alongside, same era.
A. García-Durán and M. Niepert, “Learning graph representations with embedding propagation,” in Proc. International Conference on Neural Information Processing Systems (NeurIPS) , December 2017, pp. 5125–5136
2017
Cited alongside, same era.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in Proc. International Conference on Machine Learning (ICML) , July 2019, pp. 6861–6871
2019
Later among the works it cites.
T. A. Rahman, B. Surma, M. Backes, and Y. Zhang, “Fairwalk: Towards fair graph embedding.” in Proc. International Joint Conference on Artificial Intelligence (IJCAI) , August 2019, pp. 3289–3295
2019
Later among the works it cites.
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi, “Fairness constraints: A flexible approach for fair classification,” Journal of Machine Learning Research (JMLR) , vol. 20, no. 75, pp. 1–42, 2019
2019
Later among the works it cites.
F.-Y. Sun, J. Hoffman, V. Verma, and J. Tang, “Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,” in Proc. International Conference on Learning Representations (ICLR) , May 2019
2019
Later among the works it cites.
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2017
Cited alongside, same era.
B. Hofstra, R. Corten, F. Van Tubergen, and N. B. Ellison, “Sources of segregation in social networks: A novel approach using facebook,” American Sociological Review , vol. 82, no. 3, pp. 625–656, May 2017
2017
Cited alongside, same era.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. International Conference on Neural Information Processing Systems (NeurIPS) , December 2017, pp. 1025–1035
2017
Cited alongside, same era.
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec, “Graph convolutional neural networks for web-scale recommender systems,” in Proc. ACM International Conference on Knowledge Discovery & Data Mining (SIGKDD) , July 2018, pp. 974–983
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” Proc. International Conference on Learning Representations (ICLR) , April 2018
2018
Cited alongside, same era.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018, pp. 3733–3742
2018
Cited alongside, same era.
H. Chen, B. Perozzi, Y. Hu, and S. Skiena, “Harp: Hierarchical representation learning for networks,” in Proc. AAAI Conference on Artificial Intelligence , vol. 32, no. 1, February 2018
2018
Cited alongside, same era.
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, and Y. Bengio, “Learning deep representations by mutual information estimation and maximization,” in Proc. International Conference on Learning Representations (ICLR) , April 2018
2018
Cited alongside, same era.
I. S. Gomez, B. G. da Costa, and M. A. Dos Santos, “Majorization and dynamics of continuous distributions,” Entropy , vol. 21, no. 6, p. 590, 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Proc. International Conference on Neural Information Processing Systems (NeurIPS) , vol. 32, December 2019
2019
Later among the works it cites.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in Proc. International Conference on Learning Representations (ICLR) Workshop on Representation Learning on Graphs and Manifolds , May 2019
2019
Later among the works it cites.
G. Jin, Q. Wang, C. Zhu, Y. Feng, J. Huang, and J. Zhou, “Addressing crime situation forecasting task with temporal graph convolutional neural network approach,” in Proc. International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) , February 2020, pp. 474–478
2020
Later among the works it cites.
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,” Proc. International Conference on Neural Information Processing Systems (NeurIPS) , vol. 33, December 2020
2020
Later among the works it 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 Proc. Web Conference (WWW) , April 2020, pp. 259–270
2020
Later among the works it cites.
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in Proc. International Conference on Machine Learning (ICML) , July 2020, pp. 4116–4126
2020
Later among the works it cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proc. International Conference on Machine Learning (ICML) , July 2020, pp. 1597–1607
2020
Later among the works it cites.
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in Proc. Web Conference (WWW) , April 2021
2021
Closest in time.
2021
Closest in time.
P. Li, Y. Wang, H. Zhao, P. Hong, and H. Liu, “On dyadic fairness: Exploring and mitigating bias in graph connections,” in Proc. International Conference on Learning Representations (ICLR) , April 2021
2021
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