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Hypergraphs are a common model for multiway relationships in data, and hypergraph semi-supervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes.
A wavelet tour of signal processing
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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Assortative mixing in networks
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Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
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Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
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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 · 2007
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Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing
Abderrahim Elmoataz, Olivier Lezoray, and Sébastien Bougleux · 2008
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Collective classification in network data
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Spectral clustering based on the graph p-Laplacian
Thomas Bühler and Matthias Hein · 2009
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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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A graph-theoretical approach for the analysis and model reduction of complex-balanced chemical reaction networks
Shodhan Rao, Arjan van der Schaft, and Bayu Jayawardhana · 2013
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Using local spectral methods to robustify graph-based learning algorithms
David F Gleich and Michael W Mahoney · 2015
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Algorithms for lipschitz learning on graphs
Rasmus Kyng, Anup Rao, Sushant Sachdeva, and Daniel A Spielman · 2015
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Hypergraph markov operators, eigenvalues and approximation algorithms
Anand Louis · 2015
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The network data repository with interactive graph analytics and visualization
Ryan Rossi and Nesreen Ahmed · 2015
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Higher-order organization of complex networks
Austin R Benson, David F Gleich, and Jure Leskovec · 2016
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Graph partitions and cluster synchronization in networks of oscillators
Michael T Schaub, Neave O’Clery, Yazan N Billeh, Jean-Charles Delvenne, Renaud Lambiotte, and Mauricio Barahona · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A network dynamics approach to chemical reaction networks
AJ Van der Schaft, Shodhan Rao, and Bayu Jayawardhana · 2016
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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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Re-revisiting learning on hypergraphs: confidence interval and subgradient method
Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, and TH Hubert Chan · 2017
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Simplicial closure and higher-order link prediction
Austin R Benson, Rediet Abebe, Michael T Schaub, Ali Jadbabaie, and Jon Kleinberg · 2018
Networks beyond pairwise interactions: Structure and dynamics
Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, and Giovanni Petri · 2020
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Generalizing the hypergraph laplacian via a diffusion process with mediators
T-H Hubert Chan and Zhibin Liang · 2020
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Hnhn: Hypergraph networks with hyperedge neurons
Yihe Dong, Will Sawin, and Yoshua Bengio · 2020
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Combining label propagation and simple models out-performs graph neural networks
Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, and Austin R Benson · 2020
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Ultra fine-grained image semantic embedding
Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, and Sujith Ravi · 2020
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Spectral properties of hypergraph laplacian and approximation algorithms
T-H Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang · 2018
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Predict then propagate: Graph neural networks meet personalized PageRank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering
Pan Li and Olgica Milenkovic · 2018
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Hypergraph p-Laplacian: A differential geometry view
Shota Saito, Danilo P Mandic, and Hideyuki Suzuki · 2018
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A nodal domain theorem and a higher-order Cheeger inequality for the graph p-Laplacian
Francesco Tudisco and Matthias Hein · 2018
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Decoupled smoothing on graphs
Alex Chin, Yatong Chen, Kristen M. Altenburger, and Johan Ugander · 2019
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Quadratic decomposable submodular function minimization: Theory and practice
Pan Li, Niao He, and Olgica Milenkovic · 2020
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Strongly local hypergraph diffusions for clustering and semi-supervised learning
Meng Liu, Nate Veldt, Haoyu Song, Pan Li, and David F Gleich · 2020
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Explosive higher-order Kuramoto dynamics on simplicial complexes
Ana P Millán, Joaquín J Torres, and Ginestra Bianconi · 2020
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The why, how, and when of representations for complex systems
Leo Torres, Ann S. Blevins, Danielle S. Bassett, and Tina Eliassi-Rad · 2020
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Minimizing localized ratio cut objectives in hypergraphs
Nate Veldt, Austin R Benson, and Jon Kleinberg · 2020
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Sce: Scalable network embedding from sparsest cut
Shengzhong Zhang, Zengfeng Huang, Haicang Zhou, and Ziang Zhou · 2020
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The physics of higher-order interactions in complex systems
Federico Battiston, Enrico Amico, Alain Barrat, Ginestra Bianconi, Guilherme Ferraz de Arruda, Benedetta Franceschiello, Iacopo Iacopini, Sonia Kéfi, Vito Latora, Yamir Moreno, et al · 2021
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Junteng Jia and Austin R Benson · 2021
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Consensus dynamics and opinion formation on hypergraphs
Leonie Neuhäuser, Renaud Lambiotte, and Michael T Schaub · 2021
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Learning over families of sets–hypergraph representation learning for higher order tasks
Balasubramaniam Srinivasan, Da Zheng, and George Karypis · 2021
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Nonlinear higher-order label spreading
Francesco Tudisco, Austin R Benson, and Konstantin Prokopchik · 2021
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Node and edge nonlinear eigenvector centrality for hypergraphs
Francesco Tudisco and Desmond J Higham · 2021
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