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Graph-Informed Neural Networks for Regressions on Graph-Structured Data
2022
Mathematics
In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN). This new architecture is specifically designed for regression tasks on graph-structured data that are not suitable for the well-known graph neural networks, such as the regression of functions with the domain and codomain defined on two sets of values for the vertices of a graph. In particular, we formulate a new graph-informed (GI)
doi:10.3390/math10050786
fatcat:s62o23rqzrhizdwzazidwaibvi