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A Gentle Introduction to Graph Neural Networks (2021)
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
We are starting to see practical applications in areas such as antibacterial discovery , physics simulations , fake news detection , traffic prediction and recommendation systems . This global context vector is connected to all other nodes and edges in the network, and can act as a bridge between them to pass information, building up a representation for the graph as a whole. With the following interactive figure, we explore the space of GNN architectures and the performance of this task across a few major design choices: Style of message passing, the dimensionality of embeddings, number of layers, and aggregation operation type.
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