GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering

K. Pelckmans, J. De Brabanter, J. A. Suykens, and B. De Moor, “Convex clustering shrinkage,” in Statistics and Optimization of Clustering Workshop (PASCAL)

2005

Earlier work this paper cites.

P. Diaconis and S. Janson, “Graph limits and exchangeable random graphs,” arXiv preprint arXiv:0712.2749

Original

2007

Earlier work this paper cites.

H. Ohlsson, L. Ljung, and S. Boyd, “Segmentation of ARX-models using sum-of-norms regularization,” Automatica

2010

Earlier work this paper cites.

T. D. Hocking, A. Joulin, F. Bach, and J.-P. Vert, “Clusterpath: An algorithm for clustering using convex fusion penalties,” in Intl. Conf. on Machine Learning (ICML)

2011

Earlier work this paper cites.

F. Lindsten, H. Ohlsson, and L. Ljung, “Clustering using sum-of-norms regularization: With application to particle filter output computation,” in IEEE Wrkshp. Statistical Signal Process. (SSP)

2011

Earlier work this paper cites.

American Mathematical Soc., 2012

L. Lovász, Large networks and graph limits · 2012

Earlier work this paper cites.

S. H. Chan, T. B. Costa, and E. M. Airoldi, “Estimation of exchangeable graph models by stochastic blockmodel approximation,” in IEEE Global Conf. Signal and Info. Process. (GlobalSIP)

2013

Earlier work this paper cites.

S. Chan and E. Airoldi, “A consistent histogram estimator for exchangeable graph models,” in Intl. Conf. on Machine Learning (ICML)

2014

Earlier work this paper cites.

C. Zhu, H. Xu, C. Leng, and S. Yan, “Convex optimization procedure for clustering: Theoretical revisit,” Advances in Neural Information Processing Systems

2014

Earlier work this paper cites.

K. M. Tan and D. Witten, “Statistical properties of convex clustering,” Electronic Journal of Statistics

2015

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907

Original

2016

Earlier work this paper cites.

J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Intl. Conf. on Machine Learning (ICML)

2017

Earlier work this paper cites.

H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” arXiv preprint arXiv:1710.09412

Original

2017

Cited alongside, same era.

J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang, “DeepInf: Social influence prediction with deep learning,” in Intl. Conf. on Knowledge Discovery and Data Mining (SIGKDD)

2018

Cited alongside, same era.

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?,” arXiv preprint arXiv:1810.00826

Original

2018

Cited alongside, same era.

H. Guo, Y. Mao, and R. Zhang, “Augmenting data with mixup for sentence classification: An empirical study,” arXiv preprint arXiv:1905.08941

Original

2019

Cited alongside, same era.

H. Guo, Y. Mao, and R. Zhang, “Mixup as locally linear out-of-manifold regularization,” in AAAI Conf. on Artif. Intell

H. Guo and Y. Mao, “ifMixup: Towards intrusion-free graph mixup for graph classification,” arXiv preprint arXiv:2110.09344

Original

2021

Later among the works it cites.

J. Park, H. Shim, and E. Yang, “Graph Transplant: Node saliency-guided graph mixup with local structure preservation,” arXiv preprint arXiv:2111.05639

Original

2021

Later among the works it cites.

Y. Wang, W. Wang, Y. Liang, Y. Cai, and B. Hooi, “Mixup for node and graph classification,” in Proc. World Wide Web Conf

2021

Later among the works it cites.

V. Verma, M. Qu, K. Kawaguchi, A. Lamb, Y. Bengio, J. Kannala, and J. Tang, “GraphMix: Improved training of GNNs for semi-supervised learning,” in AAAI Conf. on Artif. Intell

2021

Later among the works it cites.

Z. Mai, G. Hu, D. Chen, F. Shen, and H. T. Shen, “MetaMixUp: Learning adaptive interpolation policy of mixup with metalearning,” IEEE Trans. Neural Netw. and Learning Sys