Fetching the paper…
Reading the bibliography…
Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning.
L. Page, S. Brin, R. Motwani, T. Winograd, The pagerank citation ranking: Bringing order to the web., Tech. rep., Stanford InfoLab (1999)
1999
Earlier work this paper cites.
R. I. Kondor, J. Lafferty, Diffusion kernels on graphs and other discrete structures, in: Proceedings of the 19th international conference on machine learning, Vol. 2002, 2002, pp. 315–22
2002
Earlier work this paper cites.
X. Zhu, Z. Ghahramani, J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, in: Proceedings of the 20th International conference on Machine learning (ICML-03), 2003, pp. 912–919
2003
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, T. Eliassi-Rad, Collective classification in network data, AI magazine 29 (3) (2008) 93–93
2008
Earlier work this paper cites.
L. v. d. Maaten, G. Hinton, Visualizing data using t-sne, Journal of machine learning research 9 (Nov) (2008) 2579–2605
2008
Earlier work this paper cites.
X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp. 249–256
2010
Earlier work this paper cites.
B. Perozzi, R. Al-Rfou, 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.
2014
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, R. P. Adams, Convolutional networks on graphs for learning molecular fingerprints, in: Advances in neural information processing systems, 2015, pp. 2224–2232
2015
Earlier work this paper cites.
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, Line: Large-scale information network embedding, in: Proceedings of the 24th international conference on world wide web, 2015, pp. 1067–1077
2015
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, nature 518 (7540) (2015) 529–533
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Grover, J. Leskovec, node2vec: Scalable feature learning for networks, in: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855–864
2016
Earlier work this paper cites.
Z. Yang, W. Cohen, R. Salakhudinov, Revisiting semi-supervised learning with graph embeddings, in: International conference on machine learning, 2016, pp. 40–48
2016
Cited alongside, same era.
M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, in: Advances in neural information processing systems, 2016, pp. 3844–3852
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
P. Velickovic, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, R. D. Hjelm, Deep graph infomax., in: ICLR (Poster), 2019
2019
Later among the works it cites.
J. Klicpera, S. Weißenberger, S. Günnemann, Diffusion improves graph learning, in: Advances in Neural Information Processing Systems, 2019, pp. 13354–13366
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. G. Duran, M. Niepert, Learning graph representations with embedding propagation, in: Advances in neural information processing systems, 2017, pp. 5119–5130
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.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Caron, P. Bojanowski, A. Joulin, M. Douze, Deep clustering for unsupervised learning of visual features, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 132–149
2018
Cited alongside, same era.
Y.-H. Tang, D. Zhang, G. E. Karniadakis, An atomistic fingerprint algorithm for learning ab initio molecular force fields, The Journal of Chemical Physics 148 (3) (2018) 034101
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Later among the works it cites.
M. Fey, J. E. Lenssen, Fast graph representation learning with PyTorch Geometric, in: ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019
2019
Later among the works it cites.
Z. Peng, W. Huang, M. Luo, Q. Zheng, Y. Rong, T. Xu, 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.
2020
Closest in time.
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.