Incomplete Graph Representation and Learning via Partial Graph Neural Networks

P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine

2008

Earlier work this paper cites.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” in Advances in Neural Information Processing Systems (NIPS)

2014

Earlier work this paper cites.

D. Kingma and M. Welling, “Auto-encoding variational bayes,” in International Conference on Learning Representations (ICLR)

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR)

2015

Earlier work this paper cites.

J. McAuley, C. Targett, Q. Shi, and A. Van Den Hengel, “Image-based recommendations on styles and substitutes,” in Special Interest Group on Information Retrieval (SIGIR)

2015

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Variational graph auto-encoders,” in Advances in Neural Information Processing Systems (NIPS) Bayesian Deep Learning Workshop

2016

Earlier work this paper cites.

J. Xie, G. Ross, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in International Conference on Machine Learning (ICML)

2016

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations (ICLR)

2017

Cited alongside, same era.

F. Monti, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, and M. M. Bronstein, “Geometric deep learning on graphs and manifolds using mixture model cnns,” in IEEE conference on computer vision and pattern recognition (CVPR)

2017

Cited alongside, same era.

W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems (NIPS)

2017

Cited alongside, same era.

P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations (ICLR)

2018

Cited alongside, same era.

Z. Xinyi and L. Chen, “Capsule graph neural network,” in International Conference on Learning Representations (ICLR)

P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm, “Deep graph infomax,” in International Conference on Learning Representations (ICLR)

2019

Later among the works it cites.

L. Yang, F. Wu, Y. Wang, J. Gu, and Y. Guo, “Masked graph convolutional network.,” in International Joint Conference on Artificial Intelligence (IJCAI)

2019

Later among the works it cites.

X. Liu, X. Zhu, M. Li, L. Wang, E. Zhu, T. Liu, M. Kloft, D. Shen, J. Yin, and W. Gao, “Multiple kernel k-means with incomplete kernels,” IEEE transactions on pattern analysis and machine intelligence

2019

Later among the works it cites.

S. Geisler, D. Zügner, and S. Günnemann, “Reliable graph neural networks via robust aggregation,” in Advances in Neural Information Processing Systems (NIPS)

2020

Closest in time.

X. Chen, S. Chen, J. Yao, H. Zheng, Y. Zhang, and I. W. Tsang, “Learning on attribute-missing graphs,” IEEE Transactions on Pattern Analysis and Machine Intelligence