The Graph Neural Network Model

@article{Scarselli2009TheGN,
  title={The Graph Neural Network Model},
  author={Franco Scarselli and Marco Gori and Ah Chung Tsoi and Markus Hagenbuchner and Gabriele Monfardini},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={61-80}
}
Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. [] Key Method This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.

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