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Temporal graph networks (TGNs) have gained prominence as models for embedding dynamic interactions, but little is known about their theoretical underpinnings.
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Eta prediction with graph neural networks in google maps
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M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li · 2020
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V. Garg, S. Jegelka, and T. Jaakkola · 2020
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Graph homomorphism convolution
H. Nguyen and T. Maehara · 2020
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A. Derrow-Pinion, J. She, D. Wong, O. Lange, T. Hester, L. Perez, M. Nunkesser, S. Lee, X. Guo, B. Wiltshire, P. W. Battaglia, V. Gupta, A. Li, Z. Xu, A. Sanchez-Gonzalez, Y. Li, and P. Velickovic · 2021
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On the equivalence between temporal and static graph representations for observational predictions
J. Gao and B. Ribeiro · 2021
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Rethinking graph transformers with spectral attention
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Temporal graph network embedding with causal anonymous walks representations
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R. Sato, M. Yamada, and H. Kashima · 2021
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Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey
J. Skarding, B. Gabrys, and K. Musial · 2021
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APAN: Asynchronous propagation attention network for real-time temporal graph embedding
X. Wang, D. Lyu, M. Li, Y. Xia, Q. Yang, X. Wang, X. Wang, P. Cui, Y. Yang, B. Sun, and Z. Guo · 2021
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Modular flows: Differential molecular generation
Y. Verma, S. Kaski, M. Heinonen, and V. Garg · 2022
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Equivariant and stable positional encoding for more powerful graph neural networks
H. Wang, H. Yin, M. Zhang, and P. Li · 2022
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