Réseau neuronal de graphes récurrent

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Graph Recurrent Neural Networks

Recurrent neural networks (RNNs) are one of the most popular architectures in modern deep learning. In GNN theory, the equivalent to RNNs is represented by an architecture known as graph recurrent neural networks (GRNNs). As its name indicates, the core idea of GRNN is to generalize RNN principles used for sequential data processing to process graph data. Just like RNNs learn dependencies over sequential datasets, GRNNs can do something similar for graph-structured data. This problem can be particularly complicated in graphs as nodes can have an arbitrary number of relationships.  

Source : substack