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RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
[article]
2021
arXiv
pre-print
In recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs. The majority of GNN architectures are designed based on developing new convolutional and/or pooling layers that better extract the hidden and deeper representations of the graphs to be used for different prediction tasks. The inputs to these layers are mainly the three default descriptors of a graph, node features (X), adjacency matrix
arXiv:2109.07555v3
fatcat:xnx5yerrcnbe5kuxw5gzbzf6sq