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Hypergraphs are used to model higher-order interactions amongst agents and there exist many practically relevant instances of hypergraph datasets.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 1909
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The pagerank citation ranking: Bringing order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd · 1999
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On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung · 2003
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Scaling personalized web search
Glen Jeh and Jennifer Widom · 2003
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Beyond pairwise clustering
Sameer Agarwal, Jongwoo Lim, Lihi Zelnik-Manor, Pietro Perona, David Kriegman, and Serge Belongie · 2005
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Singular values and eigenvalues of tensors: a variational approach
Lek-Heng Lim · 2005
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Semi-supervised learning literature survey
Xiaojin Jerry Zhu · 2005
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Higher order learning with graphs
Sameer Agarwal, Kristin Branson, and Serge Belongie · 2006
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Learning with hypergraphs: Clustering, classification, and embedding
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf · 2006
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Link prediction in complex networks: A survey
Linyuan Lü and Tao Zhou · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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The total variation on hypergraphs-learning on hypergraphs revisited
Matthias Hein, Simon Setzer, Leonardo Jost, and Syama Sundar Rangapuram · 2013
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The z-eigenvalues of a symmetric tensor and its application to spectral hypergraph theory
Guoyin Li, Liqun Qi, and Gaohang Yu · 2013
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Upper bound for the largest z-eigenvalue of positive tensors
Jun He and Ting-Zhu Huang · 2014
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On spectral hypergraph theory of the adjacency tensor
Kelly J Pearson and Tan Zhang · 2014
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A provable generalized tensor spectral method for uniform hypergraph partitioning
Debarghya Ghoshdastidar and Ambedkar Dukkipati · 2015
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Multilinear pagerank
David F Gleich, Lek-Heng Lim, and Yongyang Yu · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Higher-order organization of complex networks
Austin R Benson, David F Gleich, and Jure Leskovec · 2016
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The spacey random walk: A stochastic process for higher-order data
Austin R Benson, David F Gleich, and Lek-Heng Lim · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Ubergraphs: A definition of a recursive hypergraph structure
Cliff Joslyn and Kathleen Nowak · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Inhomogeneous hypergraph clustering with applications
Pan Li and Olgica Milenkovic · 2017
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Motif clustering and overlapping clustering for social network analysis
Pan Li, Hoang Dau, Gregory Puleo, and Olgica Milenkovic · 2017
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Discovering causal signals in images
David Lopez-Paz, Robert Nishihara, Soumith Chintala, Bernhard Scholkopf, and Léon Bottou · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Tensor analysis: spectral theory and special tensors
Liqun Qi and Ziyan Luo · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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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
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
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Hyper-sagnn: a self-attention based graph neural network for hypergraphs
Ruochi Zhang, Yuesong Zou, and Jian Ma · 2019
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Pairnorm: Tackling oversmoothing in gnns
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbhakhsh, Barnabás Póczos, Ruslan Salakhutdinov, and Alexander J Smola · 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.
Community detection in hypergraphs: Optimal statistical limit and efficient algorithms
I Chien, Chung-Yi Lin, and I-Hsiang Wang · 2018
Cited alongside, same era.
Contextual stochastic block models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, and Elchanan Mossel · 2018
Cited alongside, same era.
Gvcnn: Group-view convolutional neural networks for 3d shape recognition
Yifan Feng, Zizhao Zhang, Xibin Zhao, Rongrong Ji, and Yue Gao · 2018
Cited alongside, same era.
Lingxiao Zhao and Leman Akoglu · 2019
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Clustering in graphs and hypergraphs with categorical edge labels
Ilya Amburg, Nate Veldt, and Austin Benson · 2020
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Hypersage: Generalizing inductive representation learning on hypergraphs
Devanshu Arya, Deepak K Gupta, Stevan Rudinac, and Marcel Worring · 2020
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Be more with less: Hypergraph attention networks for inductive text classification
Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, and Huan Liu · 2020
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Hnhn: Hypergraph networks with hyperedge neurons
Yihe Dong, Will Sawin, and Yoshua Bengio · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
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Residual correlation in graph neural network regression
Junteng Jia and Austion R Benson · 2020
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Distance encoding: Design provably more powerful neural networks for graph representation learning
Pan Li, Yanbang Wang, Hongwei Wang, and Jure Leskovec · 2020
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Nonlinear higher-order label spreading
Francesco Tudisco, Austin R Benson, and Konstantin Prokopchik · 2020
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Neural message passing for multi-relational ordered and recursive hypergraphs
Naganand Yadati · 2020
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Hypergraph learning with line expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao, and Tarek Abdelzaher · 2020
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Accurate learning of graph representations with graph multiset pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang · 2021
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Hypergraph convolution and hypergraph attention
Song Bai, Feihu Zhang, and Philip HS Torr · 2021
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Hypergraph clustering: from blockmodels to modularity
Philip S Chodrow, Nate Veldt, and Austin R Benson · 2021
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Unignn: a unified framework for graph and hypergraph neural networks
Jing Huang and Jie Yang · 2021
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Edge representation learning with hypergraphs
Jaehyeong Jo, Jinheon Baek, Seul Lee, Dongki Kim, Minki Kang, and Sung Ju Hwang · 2021
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim · 2021
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CopulaGNN: Towards integrating representational and correlational roles of graphs in graph neural networks
Jiaqi Ma, Bo Chang, Xuefei Zhang, and Qiaozhu Mei · 2021
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Node and edge nonlinear eigenvector centrality for hypergraphs
Francesco Tudisco and Desmond J Higham · 2021
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A nonlinear diffusion method for semi-supervised learning on hypergraphs
Francesco Tudisco, Konstantin Prokopchik, and Austin R Benson · 2021
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Session-based recommendation with hypergraph attention networks
Jianling Wang, Kaize Ding, Ziwei Zhu, and James Caverlee · 2021
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Multiplex bipartite network embedding using dual hypergraph convolutional networks
Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Yi Wei, and Yu Lin · 2021
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2032
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