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Graph representation learning has attracted lots of attention recently.
G. Jeh and J. Widom, “Scaling personalized web search,” in Proceedings of the 12th international conference on World Wide Web , 2003, pp. 271–279
2003
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2012
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2013
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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 . ACM, 2014, pp. 701–710
2014
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2014
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J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Large-scale information network embedding,” in Proceedings of the 24th international conference on world wide web , 2015, pp. 1067–1077
2015
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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
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K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034
2015
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2016
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A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2016, pp. 855–864
2016
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T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
2016
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2017
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S. Wang, L. He, B. Cao, C.-T. Lu, P. S. Yu, and A. B. Ragin, “Structural deep brain network mining,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2017, pp. 475–484
2017
Cited alongside, same era.
2017
Cited alongside, same era.
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
Cited alongside, same era.
N. Ketkar, “Introduction to pytorch,” in Deep learning with python . Springer, 2017, pp. 195–208
2017
Cited alongside, same era.
M. Qu, Y. Bengio, and J. Tang, “Gmnn: Graph markov neural networks,” in International Conference on Machine Learning , 2019, pp. 5241–5250
2019
Later among the works it cites.
L. Meng, J. yang Bai, and J. Zhang, “Latte: Application oriented social network embedding,” 2019 IEEE International Conference on Big Data (Big Data) , pp. 1169–1174, 2019
2019
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C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data , vol. 6, no. 1, p. 60, 2019
2019
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2019
Later among the works it cites.
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2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
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J. Lee, I. Lee, and J. Kang, “Self-attention graph pooling,” arXiv preprint arXiv:1904.08082 , 2019
2019
Cited alongside, same era.
Y. Jiao, Y. Xiong, J. Zhang, and Y. Zhu, “Collective link prediction oriented network embedding with hierarchical graph attention,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , 2019, pp. 419–428
2019
Cited alongside, same era.
2019
Cited alongside, same era.
F. Wu, A. H. Souza Jr, T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” in ICML , 2019
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019
2019
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L. Jing and Y. Tian, “Self-supervised visual feature learning with deep neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.
Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, and J. Huang, “Graph representation learning via graphical mutual information maximization,” in Proceedings of The Web Conference 2020 , 2020, pp. 259–270
2020
Closest in time.
2020
Closest in time.