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Graph coarsening is a technique for solving large-scale graph problems by working on a smaller version of the original graph, and possibly interpolating the results back to the original graph.
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
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Protein function prediction via graph kernels
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Cvetković, D., Rowlinson, P., and Simić, S. K · 2007
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Dhillon, I. S., Guan, Y., and Kulis, B · 2007
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Kokiopoulou, E., Chen, J., and Saad, Y · 2011
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Gromov–wasserstein distances and the metric approach to object matching
Mémoli, F · 2011
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Relaxation-based coarsening and multiscale graph organization
Ron, D., Safro, I., and Brandt, A · 2011
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Zinc: a free tool to discover chemistry for biology
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Subgraph matching kernels for attributed graphs
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A consistent histogram estimator for exchangeable graph models
Chan, S. and Airoldi, E · 2014
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Improved initialisation of model-based clustering using gaussian hierarchical partitions
Scrucca, L. and Raftery, A. E · 2015
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Graph reduction with spectral and cut guarantees
Loukas, A · 2019
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Aqsoldb, a curated reference set of aqueous solubility and 2d descriptors for a diverse set of compounds
Sorkun, M. C., Khetan, A., and Er, S · 2019
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Optimal transport for structured data with application on graphs
Titouan, V., Courty, N., Tavenard, R., and Flamary, R · 2019
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Gromov-wasserstein learning for graph matching and node embedding
Xu, H., Luo, D., Zha, H., and Carin, L · 2019
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2020
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Open graph benchmark: Datasets for machine learning on graphs
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Deep graph kernels
Yanardag, P. and Vishwanathan, S. V. N · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Do logarithmic proximity measures outperform plain ones in graph clustering?
Ivashkin, V. and Chebotarev, P · 2016
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Propagation kernels: efficient graph kernels from propagated information
Neumann, M., Garnett, R., Bauckhage, C., and Kersting, K · 2016
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Gromov-wasserstein averaging of kernel and distance matrices
Peyré, G., Cuturi, M., and Solomon, J · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Graph coarsening with preserved spectral properties
Jin, Y., Loukas, A., and JáJá, J · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Temporal graph kernels for classifying dissemination processes
Oettershagen, L., Kriege, N. M., Morris, C., and Mutzel, P · 2020
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Learning graphons via structured gromov-wasserstein barycenters
Xu, H., Luo, D., Carin, L., and Zha, H · 2020
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Graph coarsening with neural networks
Cai, C., Wang, D., and Wang, Y · 2021
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Generalized spectral clustering via gromov-wasserstein learning
Chowdhury, S. and Needham, T · 2021
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Pot: Python optimal transport
Flamary, R., Courty, N., Gramfort, A., Alaya, M. Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N. T., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D. J., Tavenard, R., Tong, A., and Vayer, T · 2021
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Scaling up graph neural networks via graph coarsening
Huang, Z., Zhang, S., Xi, C., Liu, T., and Zhou, M · 2021
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Graph condensation for graph neural networks
Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J., and Shah, N · 2021
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Graph coarsening: from scientific computing to machine learning
Chen, J., Saad, Y., and Zhang, Z · 2022
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Understanding pooling in graph neural networks
Grattarola, D., Zambon, D., Bianchi, F. M., and Alippi, C · 2022
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Semi-relaxed gromov-wasserstein divergence and applications on graphs
Vincent-Cuaz, C., Flamary, R., Corneli, M., Vayer, T., and Courty, N · 2022
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A brief survey on computational gromov-wasserstein distance
Zheng, L., Xiao, Y., and Niu, L · 2022
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