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Graph Neural Networks (GNNs) have shown promising potential in graph representation learning.
k-degenerate graphs
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The pagerank citation ranking: Bring order to the web
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Eigenvector-centrality—a node-centrality?
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A measure of centrality based on network efficiency
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State space modeling of time series
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Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
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Layer normalization, 2016
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Bresson, X. and Laurent, T · 2017
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Attention is all you need
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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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Graph neural networks for social recommendation
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., and Yin, D · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 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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Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
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Novel positional encodings to enable tree-based transformers
Shiv, V. and Quirk, C · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 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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Hippo: Recurrent memory with optimal polynomial projections
Gu, A., Dao, T., Ermon, S., Rudra, A., and Ré, C · 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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Learning to encode position for transformer with continuous dynamical model
Liu, X., Yu, H.-F., Dhillon, I., and Hsieh, C.-J · 2020
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Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Morris, C., Rattan, G., and Mutzel, P · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Order matters: Semantic-aware neural networks for binary code similarity detection
Yu, Z., Cao, R., Tang, Q., Nie, S., Huang, J., and Wu, S · 2020
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Big bird: Transformers for longer sequences
Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., et al · 2020
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On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
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Accurate learning of graph representations with graph multiset pooling
Baek, J., Kang, M., and Hwang, S. J · 2021
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Rethinking attention with performers
Choromanski, K. M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J. Q., Mohiuddin, A., Kaiser, L., Belanger, D. B., Colwell, L. J., and Weller, A · 2021
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Combining recurrent, convolutional, and continuous-time models with linear state space layers
Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., and Ré, C · 2021
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
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Coarformer: Transformer for large graph via graph coarsening
Kuang, W., Zhen, W., Li, Y., Wei, Z., and Ding, B · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
On over-squashing in message passing neural networks: The impact of width, depth, and topology
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio, P., and Bronstein, M. M · 2023
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Recurrent distance-encoding neural networks for graph representation learning
Ding, Y., Orvieto, A., He, B., and Hofmann, T · 2023
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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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Errica, F., Christiansen, H., Zaverkin, V., Maruyama, T., Niepert, M., and Alesiani, F · 2023
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Cooperative graph neural networks
Finkelshtein, B., Huang, X., Bronstein, M., and Ceylan, İ. İ · 2023
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Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2021
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Molecular graph contrastive learning with parameterized explainable augmentations
Wang, Y., Min, Y., Shao, E., and Wu, J · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Gophormer: Ego-graph transformer for node classification
Zhao, J., Li, C., Wen, Q., Wang, Y., Liu, Y., Sun, H., Xie, X., and Ye, Y · 2021
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Diffwire: Inductive graph rewiring via the lovász bound
Arnaiz-Rodríguez, A., Begga, A., Escolano, F., and Oliver, N. M · 2022
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Equivariant subgraph aggregation networks
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M. M., and Maron, H · 2022
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Hungry hungry hippos: Towards language modeling with state space models
Fu, D. Y., Dao, T., Saab, K. K., Thomas, A. W., Rudra, A., and Re, C · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Gu, A. and Dao, T · 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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Goat: A global transformer on large-scale graphs
Kong, K., Chen, J., Kirchenbauer, J., Ni, R., Bruss, C. B., and Goldstein, T · 2023
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Gapformer: Graph transformer with graph pooling for node classification
Liu, C., Zhan, Y., Ma, X., Ding, L., Tao, D., Wu, J., and Hu, W · 2023
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Attending to graph transformers
Müller, L., Galkin, M., Morris, C., and Rampášek, L · 2023
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HyenaDNA: Long-range genomic sequence modeling at single nucleotide resolution
Nguyen, E., Poli, M., Faizi, M., Thomas, A. W., Wornow, M., Birch-Sykes, C., Massaroli, S., Patel, A., Rabideau, C. M., Bengio, Y., Ermon, S., Re, C., and Baccus, S · 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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A survey on oversmoothing in graph neural networks
Rusch, T. K., Bronstein, M. M., and Mishra, S · 2023
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Large language models can be easily distracted by irrelevant context
Shi, F., Chen, X., Misra, K., Scales, N., Dohan, D., Chi, E. H., Schärli, N., and Zhou, D · 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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Modeling multivariate biosignals with graph neural networks and structured state space models
Tang, S., Dunnmon, J. A., Liangqiong, Q., Saab, K. K., Baykaner, T., Lee-Messer, C., and Rubin, D. L · 2023
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State-space models with layer-wise nonlinearity are universal approximators with exponential decaying memory
Wang, S. and Xue, B · 2023
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Kdlgt: a linear graph transformer framework via kernel decomposition approach
Wu, Y., Xu, Y., Zhu, W., Song, G., Lin, Z., Wang, L., and Liu, S · 2023
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Mambatab: A simple yet effective approach for handling tabular data
Ahamed, M. A. and Cheng, Q · 2024
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Polynormer: Polynomial-expressive graph transformer in linear time
Deng, C., Yue, Z., and Zhang, Z · 2024
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U-mamba: Enhancing long-range dependency for biomedical image segmentation
Ma, J., Li, F., and Wang, B · 2024
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S4g: Breaking the bottleneck on graphs with structured state spaces, 2024
Song, Y., Huang, S., Cai, J., Wang, X., Zhou, C., and Lin, Z · 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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Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation
Xing, Z., Ye, T., Yang, Y., Liu, G., and Zhu, L · 2024
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Vivim: a video vision mamba for medical video object segmentation
Yang, Y., Xing, Z., and Zhu, L · 2024
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