# Temporal Graph Networks for Deep Learning on Dynamic Graphs

@article{Rossi2020TemporalGN, title={Temporal Graph Networks for Deep Learning on Dynamic Graphs}, author={E. Rossi and Ben Chamberlain and F. Frasca and D. Eynard and Federico Monti and M. Bronstein}, journal={ArXiv}, year={2020}, volume={abs/2006.10637} }

Graph Neural Networks (GNNs) have become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems. Despite the plethora of different models for deep learning on graphs, few approaches have been proposed thus far for dealing with graphs that present some sort of dynamic nature (e.g. evolving features or connectivity over time). In… CONTINUE READING

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