Fetching the paper…
Reading the bibliography…
Graphs are the most ubiquitous form of structured data representation used in machine learning.
Graphs and Hypergraphs
C. Berge and E. Minieka · 1976
Earlier work this paper cites.
A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
Earlier work this paper cites.
Higher order learning with graphs
Sameer Agarwal, Kristin Branson, and Serge Belongie · 2006
Earlier work this paper cites.
Multi-way clustering using super-symmetric non-negative tensor factorization
Amnon Shashua, Ron Zass, and Tamir Hazan · 2006
Earlier work this paper cites.
Learning with hypergraphs: Clustering, classification, and embedding
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf · 2007
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Using rich social media information for music recommendation via hypergraph model
Shulong Tan, Jiajun Bu, Chun Chen, Bin Xu, Can Wang, and Xiaofei He · 2011
Earlier work this paper cites.
The total variation on hypergraphs-learning on hypergraphs revisited
Matthias Hein, Simon Setzer, Leonardo Jost, and Syama Sundar Rangapuram · 2013
Earlier work this paper cites.
The z-eigenvalues of a symmetric tensor and its application to spectral hypergraph theory
Guoyin Li, Liqun Qi, and Gaohang Yu · 2013
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
Earlier work this paper cites.
The network data repository with interactive graph analytics and visualization
Ryan A. Rossi and Nesreen K. Ahmed · 2015
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Cited alongside, same era.
Advantages to modeling relational data using hypergraphs versus graphs
Michael M Wolf, Alicia M Klinvex, and Daniel M Dunlavy · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Functional hypergraph uncovers novel covariant structures over neurodevelopment
Shi Gu, Muzhi Yang, John D Medaglia, Ruben C Gur, Raquel E Gur, Theodore D Satterthwaite, and Danielle S Bassett · 2017
h s 2 hs^{2} : Active learning over hypergraphs with pointwise and pairwise queries
I Eli Chien, Huozhi Zhou, and Pan Li · 2019
Later among the works it cites.
Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
Later among the works it cites.
Dynamic hypergraph neural networks
Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.
Hypergcn: A new method for training graph convolutional networks on hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar · 2019
Later among the works it cites.
Introducing hypergraph signal processing: theoretical foundation and practical applications
Songyang Zhang, Zhi Ding, and Shuguang Cui · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Inhomogeneous hypergraph clustering with applications
Pan Li and Olgica Milenkovic · 2017
Cited alongside, same era.
Spectral properties of hypergraph laplacian and approximation algorithms
T-H Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
Cited alongside, same era.
Hyperlearn: a distributed approach for representation learning in datasets with many modalities
Devanshu Arya, Stevan Rudinac, and Marcel Worring · 2019
Cited alongside, same era.
Beyond link prediction: Predicting hyperlinks in adjacency space
Muhan Zhang, Zhicheng Cui, Shali Jiang, and Yixin Chen
Cited in the paper.
Later among the works it cites.
Hypergraph convolution and hypergraph attention
Song Bai, Feihu Zhang, and Philip HS Torr · 2020
Closest in time.
HNHN: Hypergraph networks with hyperedge neurons
Yihe Dong, Will Sawin, and Yoshua Bengio · 2020
Closest in time.
Quantum experiments and hypergraphs: Multiphoton sources for quantum interference, quantum computation, and quantum entanglement
Xuemei Gu, Lijun Chen, and Mario Krenn · 2020
Closest in time.
Deepergcn: All you need to train deeper gcns
Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem · 2020
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Closest in time.