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This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Graph neural networks for user identity linkage
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A note on compact graphs
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A graduated assignment algorithm for graph matching
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On a relation between graph edit distance and maximum common subgraph
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A graph distance metric based on the maximal common subgraph
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The PageRank citation ranking: Bringing order to the web
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Video Google: A text retrieval approach to object matching in videos
J. Sivic and A. Zisserman · 2003
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Thirty years of graph matching in pattern recognition
D. Conte, P. Foggia, C. Sansone, and M. Vento · 2004
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Exact and approximate graph matching using random walks
M. Gori, M. Maggini, and L. Sarti · 2005
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A spectral technique for correspondence problems using pairwise constraints
M. Leordeanu and M. Hebert · 2005
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Balanced graph matching
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Modeling cellular machinery through biological network comparison
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Global alignment of multiple protein interaction networks with application to functional orthology detection
R. Singh, J. Xu, and B. Berger · 2008
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Poselets: Body part detectors trained using 3D human pose annotations
L. Bourdev and J. Malik · 2009
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Learning graph matching
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei · 2009
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A new graph-based method for pairwise global network alignment
G. W. Klau · 2009
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An integer projected fixed point method for graph matching and MAP inference
M. Leordeanu, M. Hebert, and R. Sukthankar · 2009
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Approximate graph edit distance computation by means of bipartite graph matching
K. Riesen and H. Bunke · 2009
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SCRAMSAC: Improving RANSAC’s efficiency with a spatial consistency filter
T. Sattler, B. Leibe, and L. Kobbelt · 2009
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A path following algorithm for the graph matching problem
M. Zaslavskiy, F. Bach, and J. P. Vert · 2009
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The Pascal visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
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Ranking via sinkhorn propagation
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Deep sparse rectifier neural networks
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Probability and Statistics
M. H. DeGroot and M. J. Schervish · 2012
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Network similarity decomposition (NSD): A fast and scalable approach to network alignment
G. Kollias, S. Mohammadi, and A. Grama · 2012
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Functional maps: A flexible representation of maps between shapes
M. Ovsjanikov, M. Ben-Chen, J. Solomon, A. Butscher, and L. J. Guibas · 2012
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Graph matching techniques for computer vision
M. Vento and P. Foggia · 2012
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Message-passing algorithms for sparse network alignment
M. Bayati, D. F. Gleich, A. Saberi, and Y. Wang · 2013
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Learning graphs to match
M. Cho, K. Alahari, and J. Ponce · 2013
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Partial functional correspondence
E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, and D. Cremers · 2017
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A survey on applications of bipartite graph edit distance
M. Stauffer, T. Tschachtli, A. Fischer, and K. Riesen · 2017
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Cross-lingual entity alignment via joint attribute-preserving embedding
Z. Sun, W. Hu, and C. Li · 2017
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A study of lagrangean decompositions and dual ascent solvers for graph matching
P. Swoboda, C. Rother, H. A. Ahljaija, D. Kainmueller, and B. Savchynskyy · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Convolutional set matching for graph similarity
Y. Bai, H. Ding, Y. Sun, and W. Wang · 2018
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A probabilistic approach to spectral graph matching
A. Egozi, Y. Keller, and H. Guterman · 2013
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
M. Jaggi · 2013
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Power iterated color refinement
Kristian Kersting, Martin Mladenov, Roman Garnett, and Martin Grohe · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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On convex relaxation of graph isomorphism
Y. Aflalo, A. Bronstein, and R. Kimmel · 2015
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. F. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, Ç. Gülçehre, F. Song, A. J. Ballard, J. Gilmer, G. E. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu · 2018
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A family of tractable graph distances
J. Bento and S. Ioannidis · 2018
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SplineCNN: Fast geometric deep learning with continuous B-spline kernels
M. Fey, J. E. Lenssen, F. Weichert, and H. Müller · 2018
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Graph embedding techniques, applications, and performance: A survey
P. Goyal and E. Ferrara · 2018
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Graph similarity and approximate isomorphism
M. Grohe, G. Rattan, and G. J. Woeginger · 2018
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REGAL: Representation learning-based graph alignment
M. Heimann, H. Shen, T. Safavi, and D. Koutra · 2018
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Word translation without parallel data
G. Lample, A. Conneau, M. Ranzato, L. Denoyer, and H. Jégou · 2018
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Neighbourhood consensus networks
I. Rocco, M. Cimpo, R. Arandjelović, A. Torii, T. Pajdla, and J. Sivic · 2018
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Modeling relational data with graph convolutional networks
M. S. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling · 2018
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Bootstrapping entity alignment with knowledge graph embedding
Z. Sun, W. Hu, Q. Zhang, and Y. Qu · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Cross-lingual knowledge graph alignment via graph convolutional networks
Z. Wang, Q. Lv, X. Lan, and Y. Zhang · 2018
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Representation learning on graphs with jumping knowledge networks
K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka · 2018
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Deep learning of graph matching
A. Zanfir and C. Sminchisescu · 2018
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SimGNN: A neural network approach to fast graph similarity computation
Y. Bai, H. Ding, S. Bian, T. Chen, Y. Sun, and W. Wang · 2019
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Multi-channel graph neural network for entity alignment
Y. Cao, Z. Liu, C. Li, Z. Liu, J. Li, and T. Chua · 2019
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An efficient algorithm for graph edit distance computation
X. Chen, H. Huo, J. Huan, and J. S. Vitter · 2019
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Learning edit cost estimation models for graph edit distance
X. Cortés, D. Conte, and H. Cardot · 2019
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Deep adversarial network alignment
T. Derr, H. Karimi, X. Liu, J. Xu, and J. Tang · 2019
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Fast graph representation learning with PyTorch Geometric
M. Fey and J. E. Lenssen · 2019
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Self-supervised learning of dense shape correspondence
O. Halimi, O. Litany, E. Rodolà, A. M. Bronstein, and R. Kimmel · 2019
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Graph matching networks for learning the similarity of graph structured objects
Y. Li, C. Gu, T. Dullien, O. Vinyals, and P. Kohli · 2019
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Weisfeiler and Leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe · 2019
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Relational pooling for graph representations
R. L. Murphy, B. Srinivasan, V. Rao, and B. Ribeiro · 2019
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Deep closest point: Learning representations for point cloud registration
Y. Wang and J. M. Solomon · 2019
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Relation-aware entity alignment for heterogeneous knowledge graphs
Y. Wu, X. Liu, Y. Feng, Z. Wang, R. Yan, and D. Zhao · 2019
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Deep graphical feature learning for the feature matching problem
Z. Zhang and W. S. Lee · 2019
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Neighborhood-aware attentional representation for multilingual knowledge graphs
Q. Zhu, X. Zhou, J. Wu, J. Tan, and L. Guo · 2019
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