Probabilistic Learning on Graphs via Contextual Architectures
JOURNAL OF MACHINE LEARNING RESEARCH, pp. 1-39, 2020.
We propose a novel methodology for representation learning on graph-structured data, in which a stack of Bayesian Networks learns different distributions of a vertex's neighbourhood. Through an incremental construction policy and layer-wise training, we can build deeper architectures with respect to typical graph convolutional neural netw...More
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