Bayesian nonparametric model for priors on directed graphs.
problem Constructing priors for exchangeable directed graphs.
method Infinite relational digraphon model (di-IRM) for constructing priors.
result Demonstrated inference on synthetic data.
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arXiv research
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2 results for “digraphons”
Intensity Dot Product Graphsstat.ML
IDPGs extend RDPGs with a Poisson process for random latent positions.
problem Modeling randomness in latent positions for graph structure.
method Introduce IDPGs using Poisson point processes on latent Euclidean space.
result Continuous analogues of adjacency matrices link latent structure to observed graphs.