1 research outputs found
Learning flexible representations of stochastic processes on graphs
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.
We propose a class of linear operations for stochastic (time-varying) processes
on directed (or undirected) graphs to be used in graph convolutional networks.
We propose a parameterization of such linear operations using functional
calculus to achieve arbitrarily low learning complexity. The proposed approach
is shown to model richer behaviors and display greater flexibility in learning
representations than product graph methods