DAG-GNN: DAG Structure Learning with Graph Neural Networks

arXiv: Learning, 2019.

Cited by: 50|Views45
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Abstract:

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes. A recent breakthrough formulates the problem as a continuous optimization with a structural constraint that ensures acyclicity (Z...More

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