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Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies.
Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., and Yang, Z · 2009
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R · 2012
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Learning convolutional neural networks for graphs
Niepert, M., Ahmed, M., and Kutzkov, K · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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Bresson, X. and Laurent, T · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Decoupled weight decay regularization
Loshchilov, I · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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A simple yet effective baseline for non-attributed graph classification
Cai, C. and Wang, Y · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 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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Deepgcns: Can gcns go as deep as cnns?
Li, G., Muller, M., Thabet, A., and Ghanem, B · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Graph transformer networks
Yun, S., Jeong, M., Kim, R., Kang, J., and Kim, H. J · 2019
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Graph-bert: Only attention is needed for learning graph representations
Zhang, J., Zhang, H., Xia, C., and Sun, L · 2019
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On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2020
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., and Sun, X · 2020
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 2020
Cited alongside, same era.
A fair comparison of graph neural networks for graph classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A · 2020
Cited alongside, same era.
Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2023
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Drew: Dynamically rewired message passing with delay
Gutteridge, B., Dong, X., Bronstein, M. M., and Di Giovanni, F · 2023
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A generalization of vit/mlp-mixer to graphs
He, X., Hooi, B., Laurent, T., Perold, A., LeCun, Y., and Bresson, X · 2023
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Graph inductive biases in transformers without message passing
Ma, L., Lin, C., Lim, D., Romero-Soriano, A., Dokania, P. K., Coates, M., Torr, P., and Lim, S.-N · 2023
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Attending to graph transformers
Müller, L., Galkin, M., Morris, C., and Rampášek, L · 2023
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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
Cited alongside, same era.
Distance encoding: Design provably more powerful neural networks for graph representation learning
Li, P., Wang, Y., Wang, H., and Leskovec, J · 2020
Cited alongside, same era.
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
Cited alongside, same era.
A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
Cited alongside, same era.
Graphnorm: A principled approach to accelerating graph neural network training
Cai, T., Luo, S., Xu, K., He, D., Liu, T.-y., and Wang, L · 2021
Cited alongside, same era.
Graph neural networks with learnable structural and positional representations
Dwivedi, V. P., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2021
Cited alongside, same era.
A large-scale database for graph representation learning
Freitas, S. and Dong, Y · 2021
Cited alongside, same era.
Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures
Ngo, N. K., Hy, T. S., and Kondor, R · 2023
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A critical look at the evaluation of gnns under heterophily: Are we really making progress?
Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., and Prokhorenkova, L · 2023
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Exphormer: Sparse transformers for graphs
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K · 2023
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Where did the gap go? reassessing the long-range graph benchmark
Tönshoff, J., Ritzert, M., Rosenbluth, E., and Grohe, M · 2023
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DIFFormer: Scalable (graph) transformers induced by energy constrained diffusion
Wu, Q., Yang, C., Zhao, W., He, Y., Wipf, D., and Yan, J · 2023
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Lgi-gt: Graph transformers with local and global operators interleaving
Yin, S. and Zhong, G · 2023
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Rethinking the expressive power of GNNs via graph biconnectivity
Zhang, B., Luo, S., Wang, L., and He, D · 2023
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Subgraphormer: Unifying subgraph gnns and graph transformers via graph products
Bar-Shalom, G., Bevilacqua, B., and Maron, H · 2024
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Graph mamba: Towards learning on graphs with state space models
Behrouz, A. and Hashemi, F · 2024
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Topology-informed graph transformer
Choi, Y. Y., Park, S. W., Lee, M., and Woo, Y · 2024
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Recurrent distance-encoding neural networks for graph representation learning, 2024
Ding, Y., Orvieto, A., He, B., and Hofmann, T · 2024
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Cooperative graph neural networks
Finkelshtein, B., Huang, X., Bronstein, M. M., and Ceylan, I. I · 2024
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Benchmarking positional encodings for gnns and graph transformers
Grötschla, F., Xie, J., and Wattenhofer, R · 2024
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A survey on structure-preserving graph transformers
Hoang, V. T., Lee, O., et al · 2024
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Graph external attention enhanced transformer
Liang, J., Chen, M., and Liang, J · 2024
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Understanding graph transformers by generalized propagation, 2024
Lin, C., Ma, L., Chen, Y., Ouyang, W., Bronstein, M. M., and Torr, P · 2024
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Multi-track message passing: tackling oversmoothing and oversquashing in graph learning via preventing heterophily mixing
Pei, H., Li, Y., Deng, H., Hai, J., Wang, P., Ma, J., Tao, J., Xiong, Y., and Guan, X · 2024
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A scalable and effective alternative to graph transformers
Sancak, K., Hua, Z., Fang, J., Xie, Y., Malevich, A., Long, B., Balin, M. F., and Çatalyürek, Ü. V · 2024
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Shehzad, A., Xia, F., Abid, S., Peng, C., Yu, S., Zhang, D., and Verspoor, K · 2024
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Graph-mamba: Towards long-range graph sequence modeling with selective state spaces
Wang, C., Tsepa, O., Ma, J., and Wang, B · 2024
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Position: Graph learning will lose relevance due to poor benchmarks
Bechler-Speicher, M., Finkelshtein, B., Frasca, F., Müller, L., Tönshoff, J., Siraudin, A., Zaverkin, V., Bronstein, M. M., Niepert, M., Perozzi, B., et al · 2025
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