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Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data.
Cutsets and partitions of hypergraphs
E. L. Lawler · 1973
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The ellipsoid method and its consequences in combinatorial optimization
M. Grötschel, L. Lovász, and A. Schrijver · 1981
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Modeling hypergraphs by graphs with the same mincut properties
Edmund Ihler, Dorothea Wagner, and Frank Wagner · 1993
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Between min cut and graph bisection
Dorothea Wagner and Frank Wagner · 1993
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Approximation techniques for hypergraph partitioning problems
Scott W. Hadley · 1995
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Spectral Graph Theory
Fan R. K. Chung · 1997
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Multilevel spectral hypergraph partitioning with arbitrary vertex sizes
J. Y. Zien, M. D. F. Schlag, and P. K. Chan · 1999
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Normalized cuts and image segmentation
Jianbo Shi and J. Malik · 2000
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A combinatorial algorithm minimizing submodular functions in strongly polynomial time
Alexander Schrijver · 2000
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Learning from labeled and unlabeled data using graph mincuts
Avrim Blum and Shuchi Chawla · 2001
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A flow-based method for improving the expansion or conductance of graph cuts
Kevin Lang and Satish Rao · 2004
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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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Higher order learning with graphs
Sameer Agarwal, Kristin Branson, and Serge Belongie · 2006
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Local graph partitioning using PageRank vectors
Reid Andersen, Fan Chung, and Kevin Lang · 2006
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Learning with hypergraphs: Clustering, classification, and embedding
Dengyong Zhou, Jiayuan Huang, and Bernhard Schölkopf · 2006
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Graph clustering
Satu Elisa Schaeffer · 2007
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An algorithm for improving graph partitions
Reid Andersen and Kevin J. Lang · 2008
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A faster strongly polynomial time algorithm for submodular function minimization
James B. Orlin · 2009
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Efficient minimization of decomposable submodular functions
Peter Stobbe and Andreas Krause · 2010
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Minimizing a sum of submodular functions
Vladimir Kolmogorov · 2012
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Local higher-order graph clustering
Hao Yin, Austin R. Benson, Jure Leskovec, and David F. Gleich · 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
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Generalizing the hypergraph laplacian via a diffusion process with mediators
T. H. Hubert Chan and Zhibin Liang · 2018
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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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E-tail product return prediction via hypergraph-based local graph cut
Jianbo Li, Jingrui He, and Yada Zhu · 2018
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Revisiting decomposable submodular function minimization with incidence relations
Pan Li and Olgica Milenkovic · 2018
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James B. Orlin · 2013
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Flow-based algorithms for local graph clustering
Lorenzo Orecchia and Zeyuan Allen Zhu · 2014
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Higher-order organization of complex networks
Austin R. Benson, David F. Gleich, and Jure Leskovec · 2016
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Community detection in networks: A user guide
Santo Fortunato and Darko Hric · 2016
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A simple and strongly-local flow-based method for cut improvement
Nate Veldt, David Gleich, and Michael Mahoney · 2016
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Decomposable submodular function minimization: Discrete and continuous
Alina Ene, Huy Nguyen, and László A. Végh · 2017
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Submodular hypergraphs: p-laplacians, cheeger inequalities and spectral clustering
Pan Li and Olgica Milenkovic · 2018
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Random walks on hypergraphs with edge-dependent vertex weights
Uthsav Chitra and Benjamin J. Raphael · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Flow-based local graph clustering with better seed set inclusion
Nate Veldt, Christine Klymko, and David F. Gleich · 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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Flow-based algorithms for improving clusters: A unifying framework, software, and performance, 2020
K. Fountoulakis, M. Liu, D. F. Gleich, and M. W. Mahoney · 2020
Closest in time.
Hypergraph cuts with general splitting functions, 2020
Nate Veldt, Austin R. Benson, and Jon Kleinberg · 2020
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