Fetching the paper…
Reading the bibliography…
Label spreading is a general technique for semi-supervised learning with point cloud or network data, which can be interpreted as a diffusion of labels on a graph.
Methods of combining multiple classifiers and their applications to handwriting recognition
Lei Xu, Adam Krzyzak, and Ching Y Suen · 1992
Earlier work this paper cites.
Methods of combining multiple classifiers based on different representations for pen-based handwritten digit recognition
Fevzi Alimoglu and Ethem Alpaydin · 1996
Earlier work this paper cites.
Transductive learning via spectral graph partitioning
Thorsten Joachims · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Earlier work this paper cites.
Learning with local and global consistency
Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf · 2004
Earlier work this paper cites.
Higher order learning with graphs
Sameer Agarwal, Kristin Branson, and Serge Belongie · 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.
Kernel methods in machine learning
Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola · 2008
Earlier work this paper cites.
Introduction to semi-supervised learning
Xiaojin Zhu and Andrew B Goldberg · 2009
Earlier work this paper cites.
Mnist handwritten digit database, 2010
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
Phase transition in the family of p-resistances
Morteza Alamgir and Ulrike V Luxburg · 2011
Earlier work this paper cites.
Social structure of facebook networks
Amanda L Traud, Peter J Mucha, and Mason A Porter · 2012
Earlier work this paper cites.
p-voltages: Laplacian regularization for semi-supervised learning on high-dimensional data
Nick Bridle and Xiaojin Zhu · 2013
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.
Tensor decompositions for learning latent variable models
Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky · 2014
Earlier work this paper cites.
Efficient label propagation
Yasuhiro Fujiwara and Go Irie · 2014
Earlier work this paper cites.
Using local spectral methods to robustify graph-based learning algorithms
David F Gleich and Michael W Mahoney · 2015
Earlier work this paper cites.
Algorithms for lipschitz learning on graphs
Rasmus Kyng, Anup Rao, Sushant Sachdeva, and Daniel A Spielman · 2015
Earlier work this paper cites.
Hypergraph markov operators, eigenvalues and approximation algorithms
Anand Louis · 2015
Earlier work this paper cites.
Extended discriminative random walk: a hypergraph approach to multi-view multi-relational transductive learning
Sai Nageswar Satchidanand, Harini Ananthapadmanaban, and Balaraman Ravindran · 2015
Earlier work this paper cites.
Higher-order organization of complex networks
Austin R Benson, David F Gleich, and Jure Leskovec · 2016
Earlier work this paper cites.
Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Cited alongside, same era.
Global and local information in clustering labeled block models
Varun Kanade, Elchanan Mossel, and Tselil Schramm · 2016
Cited alongside, same era.
Local algorithms for block models with side information
Elchanan Mossel and Jiaming Xu · 2016
Cited alongside, same era.
An efficient multilinear optimization framework for hypergraph matching
Quynh Nguyen, Francesco Tudisco, Antoine Gautier, and Matthias Hein · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
General tensor spectral co-clustering for higher-order data
Tao Wu, Austin R Benson, and David F Gleich · 2016
Cited alongside, same era.
Higher-order network representation learning
Ryan A Rossi, Nesreen K Ahmed, and Eunyee Koh · 2018
Later among the works it cites.
Beyond link prediction: Predicting hyperlinks in adjacency space
Muhan Zhang, Zhicheng Cui, Shali Jiang, and Yixin Chen · 2018
Later among the works it cites.
Multi-dimensional, multilayer, nonlinear and dynamic hits
Francesca Arrigo and Francesco Tudisco · 2019
Later among the works it cites.
Three hypergraph eigenvector centralities
Austin R Benson · 2019
Later among the works it cites.
Decoupled smoothing on graphs
Alex Chin, Yatong Chen, Kristen M. Altenburger, and Johan Ugander · 2019
Later among the works it cites.
Random walks on hypergraphs with edge-dependent vertex weights
Uthsav Chitra and Benjamin Raphael · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Graphlet decomposition: Framework, algorithms, and applications
Nesreen K Ahmed, Jennifer Neville, Ryan A Rossi, Nick G Duffield, and Theodore L Willke · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Block models and personalized pagerank
Isabel M Kloumann, Johan Ugander, and Jon Kleinberg · 2017
Cited alongside, same era.
Inhomogeneous hypergraph clustering with applications
Pan Li and Olgica Milenkovic · 2017
Cited alongside, same era.
Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
Later among the works it cites.
The contractivity of cone-preserving multilinear mappings
Antoine Gautier and Francesco Tudisco · 2019
Later among the works it cites.
The Perron-Frobenius theorem for multihomogeneous mappings
Antoine Gautier, Francesco Tudisco, and Matthias Hein · 2019
Later among the works it cites.
Nonlinear diffusion for community detection and semi-supervised learning
Rania Ibrahim and David Gleich · 2019
Later among the works it cites.
Optimizing generalized pagerank methods for seed-expansion community detection
Pan Li, I Chien, and Olgica Milenkovic · 2019
Later among the works it cites.
Generalized matrix means for semi-supervised learning with multilayer graphs
Pedro Mercado, Francesco Tudisco, and Matthias Hein · 2019
Later among the works it cites.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
Later among the works it cites.
Higher-order ranking and link prediction: From closing triangles to closing higher-order motifs
Ryan A Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K Ahmed, and Gang Wu · 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.
A framework for second-order eigenvector centralities and clustering coefficients
Francesca Arrigo, Desmond J Higham, and Francesco Tudisco · 2020
Closest in time.
Higher-order label homogeneity and spreading in graphs
Dhivya Eswaran, Srijan Kumar, and Christos Faloutsos · 2020
Closest in time.
Using cliques with higher-order spectral embeddings improves graph visualizations
Huda Nassar, Caitlin Kennedy, Shweta Jain, Austin R Benson, and David Gleich · 2020
Closest in time.
Hypergraph cuts with general splitting functions
Nate Veldt, Austin R Benson, and Jon Kleinberg · 2020
Closest in time.
Localized flow-based clustering in hypergraphs
Nate Veldt, Austin R Benson, and Jon Kleinberg · 2020
Closest in time.