Hyper-SAGNN: a self-attention based graph neural network for hypergraphs

Learning with hypergraphs: Clustering, classification, and embedding

D. Zhou, J. Huang, and B. Schölkopf · 2007

Earlier work this paper cites.

Hypergraph spectral learning for multi-label classification

L. Sun, S. Ji, and J. Ye · 2008

Earlier work this paper cites.

Comprehensive mapping of long-range interactions reveals folding principles of the human genome

E. Lieberman-Aiden, N. L. Van Berkum, L. Williams, M. Imakaev, T. Ragoczy, A. Telling, I. Amit, B. R. Lajoie, P. J. Sabo, M. O. Dorschner, et al · 2009

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Collaborative filtering meets mobile recommendation: A user-centered approach

V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang · 2010

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Translating embeddings for modeling multi-relational data

A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013

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Deepwalk: Online learning of social representations

B. Perozzi, R. Al-Rfou, and S. Skiena · 2014

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A 3d map of the human genome at kilobase resolution reveals principles of chromatin looping

S. S. Rao, M. H. Huntley, N. C. Durand, E. K. Stamenova, I. D. Bochkov, J. T. Robinson, A. L. Sanborn, I. Machol, A. D. Omer, E. S. Lander, et al · 2014

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The movielens datasets: History and context

F. M. Harper and J. A. Konstan · 2015

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node2vec: Scalable feature learning for networks

A. Grover and J. Leskovec · 2016

Cited alongside, same era.

Large-scale embedding learning in heterogeneous event data

H. Gui, J. Liu, F. Tao, M. Jiang, B. Norick, and J. Han · 2016

Cited alongside, same era.

Semi-supervised classification with graph convolutional networks

Original

T. N. Kipf and M. Welling · 2016

Cited alongside, same era.

Cell-cycle dynamics of chromosomal organization at single-cell resolution

T. Nagano, Y. Lubling, C. Várnai, C. Dudley, W. Leung, Y. Baran, N. M. Cohen, S. Wingett, P. Fraser, and A. Tanay · 2017

Cited alongside, same era.

Massively multiplex single-cell hi-c

V. Ramani, X. Deng, R. Qiu, K. L. Gunderson, F. J. Steemers, C. M. Disteche, W. S. Noble, Z. Duan, and J. Shendure · 2017

Cited alongside, same era.

Attention is all you need

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017

Learning on partial-order hypergraphs

F. Feng, X. He, Y. Liu, L. Nie, and T.-S. Chua · 2018

Later among the works it cites.

Unsupervised embedding of single-cell hi-c data

J. Liu, D. Lin, G. G. Yardımcı, and W. S. Noble · 2018

Later among the works it cites.

Umap: Uniform manifold approximation and projection for dimension reduction

Original

L. McInnes, J. Healy, and J. Melville · 2018

Later among the works it cites.

Structural deep embedding for hyper-networks

K. Tu, P. Cui, X. Wang, F. Wang, and W. Zhu · 2018

Later among the works it cites.

Hypergcn: Hypergraph convolutional networks for semi-supervised classification

Original

N. Yadati, M. Nimishakavi, P. Yadav, A. Louis, and P. Talukdar · 2018

Later among the works it cites.

Hyper2vec: Biased random walk for hyper-network embedding