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A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.
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A graduated assignment algorithm for graph matching
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Applications and explanations of zipf’s law
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A (sub) graph isomorphism algorithm for matching large graphs
Cordella, L. P., Foggia, P., Sansone, C., and Vento, M · 2004
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Modeling cellular machinery through biological network comparison
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Visualizing data using t-SNE
Maaten, L. v. d. and Hinton, G · 2008
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Gromov-Hausdorff distances in Euclidean spaces
Mémoli, F · 2008
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Global alignment of multiple protein interaction networks with application to functional orthology detection
Singh, R., Xu, J., and Berger, B · 2008
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Optimal transport: Old and new , volume 338
Villani, C · 2008
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Algorithms for large, sparse network alignment problems
Bayati, M., Gerritsen, M., Gleich, D. F., Saberi, A., and Wang, Y · 2009
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Spectral Gromov-Wasserstein distances for shape matching
Mémoli, F · 2009
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BPR: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L · 2009
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A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching
Bronstein, A. M., Bronstein, M. M., Kimmel, R., Mahmoudi, M., and Sapiro, G · 2010
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Topological network alignment uncovers biological function and phylogeny
Kuchaiev, O., Milenković, T., Memišević, V., Hayes, W., and Pržulj, N · 2010
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Point set registration: Coherent point drift
Myronenko, A. and Song, X · 2010
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Integrative network alignment reveals large regions of global network similarity in yeast and human
Kuchaiev, O. and Pržulj, N · 2011
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Gromov-Wasserstein distances and the metric approach to object matching
Mémoli, F · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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NETAL: A new graph-based method for global alignment of protein–protein interaction networks
Neyshabur, B., Khadem, A., Hashemifar, S., and Arab, S. S · 2013
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A unified convergence analysis of block successive minimization methods for nonsmooth optimization
Razaviyayn, M., Hong, M., and Luo, Z.-Q · 2013
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Hubalign: An accurate and efficient method for global alignment of protein–protein interaction networks
Hashemifar, S. and Xu, J · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Multi-graph matching via affinity optimization with graduated consistency regularization
Yan, J., Cho, M., Zha, H., Yang, X., and Chu, S. M · 2016
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Altschuler, J., Weed, J., and Rigollet, P · 2017
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Sequential graph matching with sequential monte carlo
Jun, S.-H., Wong, S. W., Zidek, J., and Bouchard-Côté, A · 2017
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Triangular alignment TAME: A tensor-based approach for higher-order network alignment
Mohammadi, S., Gleich, D. F., Kolda, T. G., and Grama, A · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
Monti, F., Bronstein, M., and Bresson, X · 2017
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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Iterative Bregman projections for regularized transportation problems
Benamou, J.-D., Carlier, G., Cuturi, M., Nenna, L., and Peyré, G · 2015
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L-GRAAL: Lagrangian graphlet-based network aligner
Malod-Dognin, N. and Pržulj, N · 2015
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Simultaneous optimization of both node and edge conservation in network alignment via WAVE
Sun, Y., Crawford, J., Tang, J., and Milenković, T · 2015
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MAGNA++: Maximizing accuracy in global network alignment via both node and edge conservation
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Gromov-Wasserstein alignment of word embedding spaces
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Learning generative models across incomparable spaces
Bunne, C., Alvarez-Melis, D., Krause, A., and Jegelka, S · 2018
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The Gromov-Wasserstein distance between networks and stable network invariants
Chowdhury, S. and Mémoli, F · 2018
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Mixed membership word embeddings for computational social science
Foulds, J · 2018
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Unsupervised alignment of embeddings with Wasserstein Procrustes
Grave, E., Joulin, A., and Berthet, Q · 2018
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Anonymous walk embeddings
Ivanov, S. and Burnaev, E · 2018
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Low rank spectral network alignment
Nassar, H., Veldt, N., Mohammadi, S., Grama, A., and Gleich, D. F · 2018
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Vayer, T., Chapel, L., Flamary, R., Tavenard, R., and Courty, N · 2018
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A fast proximal point method for Wasserstein distance
Xie, Y., Wang, X., Wang, R., and Zha, H · 2018
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Distilled Wasserstein learning for word embedding and topic modeling
Xu, H., Wang, W., Liu, W., and Carin, L · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
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Generalizing graph matching beyond quadratic assignment model
Yu, T., Yan, J., Wang, Y., Liu, W., et al · 2018
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