The authors propose DCN and PDCN, new GNN architectures using causal graph filters for convolutional learning on DAGs, with established equivariance properties and competitive empirical performance.
From correlation to causation networks: A simple approximate learning algorithm and its application to high-dimensional plant gene expression data
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Directed Acyclic Graph Convolutional Networks
The authors propose DCN and PDCN, new GNN architectures using causal graph filters for convolutional learning on DAGs, with established equivariance properties and competitive empirical performance.