
Sensitivity of Uncertainty Propagation for the Elliptic Diffusion Equation
For elliptic diffusion equations with random coefficient and source term...
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Diffusion Based Network Embedding
In network embedding, random walks play a fundamental role in preserving...
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The Elephant Quantum Walk
We explore the impact of longrange memory on the properties of a family...
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Quantum diffusion map for nonlinear dimensionality reduction
Inspired by random walk on graphs, diffusion map (DM) is a class of unsu...
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Online Factorization and Partition of Complex Networks From Random Walks
Finding the reduceddimensional structure is critical to understanding c...
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Network navigation using Page Rank random walks
We introduce a formalism based on a continuous time approximation, to st...
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PAN: Path Integral Based Convolution for Deep Graph Neural Networks
Convolution operations designed for graphstructured data usually utiliz...
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Markov Random Walk Representations with Continuous Distributions
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the data density. We relate this diffusion equation to a path integral and derive the corresponding path probability measure. The framework is useful for incorporating continuous data densities and prior knowledge.
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