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Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics.
Solving Ordinary Differential Equations I: Nonstiff Problems , volume 8
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Kipf, T. N. and Welling, M · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
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Graph attention networks
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Long short-term memory and learning-to-learn in networks of spiking neurons
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
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Neural relational inference for interacting systems
Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., and Zemel, R · 2018
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Continuous graph neural networks
Xhonneux, L.-P., Qu, M., and Tang, J · 2020
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Generalizing graph neural networks beyond homophily
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., and Koutra, D · 2020
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Grand: Graph neural diffusion
Chamberlain, B., Rowbottom, J., Gorinova, M. I., Bronstein, M., Webb, S., and Rossi, E · 2021
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Probabilistic numeric convolutional neural networks
Finzi, M. A., Bondesan, R., and Welling, M · 2021
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Explainable multivariate time series classification: a deep neural network which learns to attend to important variables as well as time intervals
Hsieh, T.-Y., Wang, S., Sun, Y., and Honavar, V · 2021
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Spiking deep residual networks
Hu, Y., Tang, H., and Pan, G · 2021
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Simplifying graph convolutional networks
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How powerful are graph neural networks?
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Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
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Adaptive universal generalized pagerank graph neural network
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Influential nodes detection in dynamic social networks: A survey
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Learning modular simulations for homogeneous systems
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Graph-coupled oscillator networks
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Spiking graph convolutional networks
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Training spiking neural networks using lessons from deep learning
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Scaling up dynamic graph representation learning via spiking neural networks
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