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Gromov-Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g.
Optimal and suboptimal algorithms for the quadratic assignment problem
Paul C Gilmore · 1962
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Groups of polynomial growth and expanding maps
Mikhael Gromov · 1981
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Probabilistic asymptotic properties of some combinatorial optimization problems
Rainer E Burkard and Ulrich Fincke · 1985
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On a relation between graph edit distance and maximum common subgraph
Horst Bunke · 1997
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On the optimal covering of equal metric balls in a sphere
Min-Shik Cho · 1997
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The quadratic assignment problem
Rainer E Burkard, Eranda Cela, Panos M Pardalos, and Leonidas S Pitsoulis · 1998
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Differential and numerically invariant signature curves applied to object recognition
Eugenio Calabi, Peter J Olver, Chehrzad Shakiban, Allen Tannenbaum, and Steven Haker · 1998
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Collective dynamics of ‘small-world’networks
Duncan J Watts and Steven H Strogatz · 1998
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Distances between Banach spaces
Nigel J Kalton and Mikhail I Ostrovskii · 1999
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Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
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Intrinsic dimension estimation using packing numbers
Balázs Kégl · 2002
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Maximum common subgraph isomorphism algorithms for the matching of chemical structures
John W Raymond and Peter Willett · 2002
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Detection of abnormal change in a time series of graphs
Peter Shoubridge, Miro Kraetzl, WAL Wallis, and Horst Bunke · 2002
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Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
Nathalie Tzourio-Mazoyer, Brigitte Landeau, Dimitri Papathanassiou, Fabrice Crivello, Olivier Etard, Nicolas Delcroix, Bernard Mazoyer, and Marc Joliot · 2002
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Scale-free networks are ultrasmall
Reuven Cohen and Shlomo Havlin · 2003
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Topics in optimal transportation
Cédric Villani · 2003
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Classification and detection of abnormal events in time series of graphs
H Bunke and M Kraetzl · 2004
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Thirty years of graph matching in pattern recognition
Donatello Conte, Pasquale Foggia, Carlo Sansone, and Mario Vento · 2004
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The enron corpus: A new dataset for email classification research
Bryan Klimt and Yiming Yang · 2004
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Comparing point clouds
Facundo Mémoli and Guillermo Sapiro · 2004
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Anomaly detection in time series of graphs using arma processes
Brandon Pincombe · 2005
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Computer network monitoring and abnormal event detection using graph matching and multidimensional scaling
Horst Bunke, Peter Dickinson, Andreas Humm, Ch Irniger, and Miro Kraetzl · 2006
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Persistent network homology from the perspective of dendrograms
Hyekyoung Lee, Moo K Chung, Hyejin Kang, Boong-Nyun Kim, Dong Soo Lee, et al · 2006
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On the geometry of metric measure spaces
Karl-Theodor Sturm · 2006
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Efficiency and cost of economical brain functional networks
Sophie Achard and Ed Bullmore · 2007
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Metric structures for Riemannian and non-Riemannian spaces
Mikhail Gromov · 2007
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A phase transition for the diameter of the configuration model
Remco van der Hofstad, Gerard Hooghiemstra, and Dmitri Znamenski · 2007
Deltacon: A principled massive-graph similarity function
Danai Koutra, Joshua T Vogelstein, and Christos Faloutsos · 2013
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The Gromov-Hausdorff distance: a brief tutorial on some of its quantitative aspects
Facundo Mémoli · 2013
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Altered functional and structural brain network organization in autism
Jeffrey D Rudie, JA Brown, Devi Beck-Pancer, LM Hernandez, EL Dennis, PM Thompson, SY Bookheimer, and MJNC Dapretto · 2013
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Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder
Elizabeth Redcay, Joseph M Moran, Penelope Lee Mavros, Helen Tager-Flusberg, John DE Gabrieli, and Susan Whitfield-Gabrieli · 2013
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The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
Adriana Di Martino, Chao-Gan Yan, Qingyang Li, Erin Denio, Francisco X Castellanos, Kaat Alaerts, Jeffrey S Anderson, Michal Assaf, Susan Y Bookheimer, Mirella Dapretto, et al · 2014
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Cited alongside, same era.
On the use of Gromov-Hausdorff distances for shape comparison
Facundo Mémoli · 2007
Cited alongside, same era.
Exploring network structure, dynamics, and function using NetworkX
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
Cited alongside, same era.
Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease
Kaustubh Supekar, Vinod Menon, Daniel Rubin, Mark Musen, and Michael D Greicius · 2008
Cited alongside, same era.
Gromov-Hausdorff stable signatures for shapes using persistence
Frédéric Chazal, David Cohen-Steiner, Leonidas J Guibas, Facundo Mémoli, and Steve Y Oudot · 2009
Cited alongside, same era.
LoOP: local outlier probabilities
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek · 2009
Cited alongside, same era.
Spectral Gromov-Wasserstein distances for shape matching
Facundo Mémoli · 2009
Cited alongside, same era.
Graph matching and learning in pattern recognition in the last 10 years
Pasquale Foggia, Gennaro Percannella, and Mario Vento · 2014
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On convex relaxation of graph isomorphism
Yonathan Aflalo, Alexander Bronstein, and Ron Kimmel · 2015
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Using Gromov-Wasserstein distance to explore sets of networks
Reigo Hendrikson et al · 2016
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Network-wide anomaly detection via the Dirichlet process
Nick Heard and Patrick Rubin-Delanchy · 2016
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Cyber security data sources for dynamic network research
Alexander D Kent · 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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Who invented the Gromov-Hausdorff distance?
Alexey A Tuzhilin · 2016
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A polynomial-time relaxation of the Gromov-Hausdorff distance
Soledad Villar, Afonso S Bandeira, Andrew J Blumberg, and Rachel Ward · 2016
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Computational aspects of the Gromov-Hausdorff distance and its application in non-rigid shape matching
Felix Schmiedl · 2017
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Computing the Gromov-Hausdorff distance for metric trees
Pankaj K Agarwal, Kyle Fox, Abhinandan Nath, Anastasios Sidiropoulos, and Yusu Wang · 2018
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PyNomaly: Anomaly detection using local outlier probabilities (loOP)
Valentino Constantinou · 2018
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Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification
Amirali Kazeminejad and Roberto C Sotero · 2019
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Approximating Gromov-Hausdorff distance in euclidean space
Sushovan Majhi, Jeffrey Vitter, and Carola Wenk · 2019
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Scikit-TDA: topological data analysis for python
Nathaniel Saul and Chris Tralie · 2019
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Lipschitz (non-)equivalence of the Gromov–Hausdorff distances, including on ultrametric spaces
Vladyslav Oles and Kevin R Vixie · 2022
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