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We establish a bridge between spectral clustering and Gromov-Wasserstein Learning (GWL), a recent optimal transport-based approach to graph partitioning.
Algebraic connectivity of graphs
Miroslav Fiedler · 1973
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Efficient Monte Carlo procedures for generating points uniformly distributed over bounded regions
Robert L Smith · 1984
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Spectral convergence of Riemannian manifolds
Atsushi Kasue and Hironori Kumura · 1994
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Community structure in social and biological networks
Michelle Girvan and Mark EJ Newman · 2002
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Finding community structure in very large networks
Aaron Clauset, Mark EJ Newman, and Cristopher Moore · 2004
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Brenda, the enzyme database: updates and major new developments
Ida Schomburg, Antje Chang, Christian Ebeling, Marion Gremse, Christian Heldt, Gregor Huhn, and Dietmar Schomburg · 2004
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Laplacians and the Cheeger inequality for directed graphs
Fan Chung · 2005
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Laplace–Beltrami spectra as ‘Shape-DNA’of surfaces and solids
Martin Reuter, Franz-Erich Wolter, and Niklas Peinecke · 2006
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On the geometry of metric measure spaces
Karl-Theodor Sturm · 2006
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On the use of Gromov-Hausdorff distances for shape comparison
Facundo Mémoli · 2007
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Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre · 2008
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Maps of random walks on complex networks reveal community structure
Martin Rosvall and Carl T Bergstrom · 2008
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A concise and provably informative multi-scale signature based on heat diffusion
Jian Sun, Maks Ovsjanikov, and Leonidas Guibas · 2009
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Global network alignment using multiscale spectral signatures
Rob Patro and Carl Kingsford · 2012
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The space of spaces: curvature bounds and gradient flows on the space of metric measure spaces
Karl-Theodor Sturm · 2012
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The diffusion of microfinance
Abhijit Banerjee, Arun G Chandrasekhar, Esther Duflo, and Matthew O Jackson · 2013
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Heat kernel coupling for multiple graph analysis
Michael M Bronstein and Klaus Glashoff · 2013
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Modelling convex shape priors and matching based on the Gromov-Wasserstein distance
Bernhard Schmitzer and Christoph Schnörr · 2013
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Stable and informative spectral signatures for graph matching
Nan Hu, Raif M Rustamov, and Leonidas Guibas · 2014
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Gromov-Monge quasi-metrics and distance distributions
Facundo Mémoli and Tom Needham · 2018
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Low rank spectral network alignment
Huda Nassar, Nate Veldt, Shahin Mohammadi, Ananth Grama, and David F Gleich · 2018
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NetLSD: hearing the shape of a graph
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander Bronstein, and Emmanuel Müller · 2018
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Titouan Vayer, Laetita Chapel, Rémi Flamary, Romain Tavenard, and Nicolas Courty · 2018
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Towards optimal transport with global invariances
David Alvarez-Melis, Stefanie Jegelka, and Tommi S Jaakkola · 2019
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Jure Leskovec and Andrej Krevl · 2014
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Pinar Yanardag and SVN Vishwanathan · 2015
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Using Gromov-Wasserstein distance to explore sets of networks
Reigo Hendrikson · 2016
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Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
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Gromov-Wasserstein averaging of kernel and distance matrices
Gabriel Peyré, Marco Cuturi, and Justin Solomon · 2016
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Entropic metric alignment for correspondence problems
Justin Solomon, Gabriel Peyré, Vladimir G Kim, and Suvrit Sra · 2016
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Transport optimal de mesures positives: modèles, méthodes numériques, applications
Lénaïc Chizat · 2017
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Learning generative models across incomparable spaces
Charlotte Bunne, David Alvarez-Melis, Andreas Krause, and Stefanie Jegelka · 2019
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The Gromov–Wasserstein distance between networks and stable network invariants
Samir Chowdhury and Facundo Mémoli · 2019
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Graph diffusion Wasserstein distances
Amélie Barbe, Marc Sebban, Paulo Gonçalves, Pierre Borgnat, and Rémi Gribonval · 2020
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Partial optimal tranport with applications on positive-unlabeled learning
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Gromov-Wasserstein averaging in a Riemannian framework
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Entropy-transport distances between unbalanced metric measure spaces
Nicoló De Ponti and Andrea Mondino · 2020
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COPT: Coordinated optimal transport on graphs
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The Unbalanced Gromov Wasserstein distance: Conic formulation and relaxation
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The shape of data: Intrinsic distance for data distributions
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, and Emmanuel Müller · 2020
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Gromov-Wasserstein factorization models for graph clustering
Hongteng Xu · 2020
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Learning autoencoders with relational regularization
Hongteng Xu, Dixin Luo, Ricardo Henao, Svati Shah, and Lawrence Carin · 2020
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