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Graph classification is a challenging research problem in many applications across a broad range of domains.
Maximum likelihood from incomplete data via the EM algorithm
Dempster, A. P., Laird, N. M., and Rubin, D. B · 1977
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Adaptive mixtures of local experts
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Mixtures of gaussian processes
TRESP, V · 2001
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Smote: synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P · 2002
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A parallel mixture of svms for very large scale problems
Collobert, R., Bengio, S., and Bengio, Y · 2002
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
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Borderline-smote: a new over-sampling method in imbalanced data sets learning
Han, H., Wang, W.-Y., and Mao, B.-H · 2005
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Adasyn: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., and Li, S · 2008
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Learning from imbalanced data
He, H. and Garcia, E. A · 2009
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Classification of imbalanced data: A review
Sun, Y., Wong, A. K., and Kamel, M. S · 2009
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Learning factored representations in a deep mixture of experts
Eigen, D., Ranzato, M., and Sutskever, I · 2013
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Mixture of experts: a literature survey
Masoudnia, S. and Ebrahimpour, R · 2014
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Subset feature learning for fine-grained category classification
Ge, Z., McCool, C., Sanderson, C., and Corke, P · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Joint structure feature exploration and regularization for multi-task graph classification
Pan, S., Wu, J., Zhu, X., Zhang, C., and Philip, S. Y · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S · 2015
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Fine-grained classification via mixture of deep convolutional neural networks
Ge, Z., Bewley, A., McCool, C., Corke, P., Upcroft, B., and Sanderson, C · 2016
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Learning deep representation for imbalanced classification
Huang, C., Li, Y., Loy, C. C., and Tang, X · 2016
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Task sensitive feature exploration and learning for multitask graph classification
Pan, S., Wu, J., Zhu, X., Long, G., and Zhang, C · 2016
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Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Focal loss for dense object detection
Lin, T., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J · 2017
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Fey, M., Lenssen, J. E., Weichert, F., and Müller, H · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., and Chi, E. H · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Inductive matrix completion based on graph neural networks
Zhang, M. and Chen, Y · 2019
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Optimal transport graph neural networks
Bécigneul, G., Ganea, O.-E., Chen, B., Barzilay, R., and Jaakkola, T · 2020
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G., Frasca, F., Zafeiriou, S., and Bronstein, M. M · 2020
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Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
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Remix: Rebalanced mixup
Chou, H.-P., Chang, S.-C., Pan, J.-Y., Wei, W., and Juan, D.-C · 2020
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Moleculenet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Graph u-nets
Gao, H. and Ji, S · 2019
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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Bayesian graph neural networks with adaptive connection sampling
Hasanzadeh, A., Hajiramezanali, E., Boluki, S., Zhou, M., Duffield, N., Narayanan, K., and Qian, X · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification
Jia, Z., Lin, Y., Wang, J., Zhou, R., Ning, X., He, Y., and Zhao, Y · 2020
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M2m: Imbalanced classification via major-to-minor translation
Kim, J., Jeong, J., and Shin, J · 2020
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FLAG: adversarial data augmentation for graph neural networks
Kong, K., Li, G., Ding, M., Wu, Z., Zhu, C., Ghanem, B., Taylor, G., and Goldstein, T · 2020
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Deep representation learning on long-tailed data: A learnable embedding augmentation perspective
Liu, J., Sun, Y., Han, C., Dou, Z., and Li, W · 2020
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Rethinking pooling in graph neural networks
Mesquita, D., Souza, A., and Kaski, S · 2020
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Multitask mixture of sequential experts for user activity streams
Qin, Z., Cheng, Y., Zhao, Z., Chen, Z., Metzler, D., and Qin, J · 2020
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Multi-class imbalanced graph convolutional network learning
Shi, M., Tang, Y., Zhu, X., Wilson, D., and Liu, J · 2020
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Equalization loss for long-tailed object recognition
Tan, J., Wang, C., Li, B., Li, Q., Ouyang, W., Yin, C., and Yan, J · 2020
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Learning imbalanced datasets with label-distribution-aware margin loss
Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R · 2020
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Haar graph pooling
Wang, Y. G., Li, M., Ma, Z., Montufar, G., Zhuang, X., and Fan, Y · 2020
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
Fedus, W., Zoph, B., and Shazeer, N · 2021
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Wasserstein embedding for graph learning
Kolouri, S., Naderializadeh, N., Rohde, G. K., and Hoffmann, H · 2021
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