Learning flexible representations of stochastic processes on graphs

3 Nov 2017 Addison Bohannon Brian Sadler Radu Balan

Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs... (read more)

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