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This work formalizes the associational task of predicting node attribute evolution in temporal graphs from the perspective of learning equivariant representations.
A reduction of a graph to a canonical form and an algebra arising during this reduction
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Graph analysis of functional brain networks: practical issues in translational neuroscience
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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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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. S · 2016
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Neural message passing for quantum chemistry
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Attention is all you need
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Gc-lstm: Graph convolution embedded lstm for dynamic link prediction
Chen, J., Xu, X., Wu, Y., and Zheng, H · 2018
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Dyngem: Deep embedding method for dynamic graphs
Goyal, P., Kamra, N., He, X., and Liu, Y · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2018
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Dylink2vec: Effective feature representation for link prediction in dynamic networks
Rahman, M., Saha, T. K., Hasan, M. A., Xu, K. S., and Reddy, C. K · 2018
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Structured sequence modeling with graph convolutional recurrent networks
Seo, Y., Defferrard, M., Vandergheynst, P., and Bresson, X · 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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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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Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Yu, B., Yin, H., and Zhu, Z · 2018
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Supervised community detection with line graph neural networks
Chen, Z., Li, L., and Bruna, J · 2019
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On the equivalence between graph isomorphism testing and function approximation with gnns
Chen, Z., Villar, S., Chen, L., and Bruna, J · 2019
Addressing crime situation forecasting task with temporal graph convolutional neural network approach
Jin, G., Wang, Q., Zhu, C., Feng, Y., Huang, J., and Zhou, J · 2020
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Examining covid-19 forecasting using spatio-temporal graph neural networks
Kapoor, A., Ben, X., Liu, L., Perozzi, B., Barnes, M., Blais, M., and O’Banion, S · 2020
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What graph neural networks cannot learn: depth vs width
Loukas, A · 2020
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Temporal multi-graph convolutional network for traffic flow prediction
Lv, M., Hong, Z., Chen, L., Chen, T., Zhu, T., and Ji, S · 2020
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Dynamic graph convolutional networks
Manessi, F., Rozza, A., and Manzo, M · 2020
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Graph homomorphism convolution
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Understanding the representation power of graph neural networks in learning graph topology
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Graph message passing with cross-location attentions for long-term ili prediction
Deng, S., Wang, S., Rangwala, H., Wang, L., and Ning, Y · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H · 2019
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Variational graph recurrent neural networks
Hajiramezanali, E., Hasanzadeh, A., Narayanan, K. R., Duffield, N., Zhou, M., and Qian, X · 2019
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Universal invariant and equivariant graph neural networks
Keriven, N. and Peyré, G · 2019
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On the expressive power of deep polynomial neural networks
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Transfer graph neural networks for pandemic forecasting
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Evolvegcn: Evolving graph convolutional networks for dynamic graphs
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Temporal graph networks for deep learning on dynamic graphs
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., and Bronstein, M. M · 2020
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Dysat: Deep neural representation learning on dynamic graphs via self-attention networks
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On the equivalence between positional node embeddings and structural graph representations
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Towards scale-invariant graph-related problem solving by iterative homogeneous gnns
Tang, H., Huang, Z., Gu, J., Lu, B.-L., and Su, H · 2020
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Ubaru, S., Horesh, L., and Cohen, G · 2020
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Are transformers universal approximators of sequence-to-sequence functions?
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T-gcn: A temporal graph convolutional network for traffic prediction
Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., and Li, H · 2020
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Weisfeiler and lehman go cellular: Cw networks
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Reconstruction for powerful graph representations
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Temporal heterogeneous information network embedding
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Deep constraint-based propagation in graph neural networks
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Inductive representation learning in temporal networks via causal anonymous walks
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Discrete-time temporal network embedding via implicit hierarchical learning in hyperbolic space
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