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- W4205655454 abstract "We present a geometrically enhanced Markov chain Monte Carlo sampler for networks based on a discrete curvature measure defined on graphs. Specifically, we incorporate the concept of graph Forman curvature into sampling procedures on both the nodes and edges of a network explicitly, via the transition probability of the Markov chain, as well as implicitly, via the target stationary distribution, which gives a novel, curved Markov chain Monte Carlo approach to learning networks. We show that integrating curvature into the sampler results in faster convergence to a wide range of network statistics demonstrated on deterministic networks drawn from real-world data." @default.
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- W4205655454 date "2022-01-01" @default.
- W4205655454 modified "2023-09-25" @default.
- W4205655454 title "Curved Markov Chain Monte Carlo for Network Learning" @default.
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- W4205655454 doi "https://doi.org/10.1007/978-3-030-93413-2_39" @default.
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