Continuous-time dynamic GNNs built from static universal approximators and recurrent updates match a new continuous-time 1-WL test and inherit universal approximation guarantees, even on disconnected asynchronous graphs.
In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=lxHgXYN4bwl
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Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks
Continuous-time dynamic GNNs built from static universal approximators and recurrent updates match a new continuous-time 1-WL test and inherit universal approximation guarantees, even on disconnected asynchronous graphs.