A unified tensor framework models higher-order Markov chains with memory via an even-order paired tensor linking folded and unfolded dynamics, with approximation to low-dimensional nonlinear systems and application to hypergraph random walks.
What are higher-order networks?
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
eess.SY 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Under a tensor generalized detailed-balance condition, tensor-coupled flow-conservation systems on hypergraphs have a unique equilibrium with global asymptotic stability via an entropy Lyapunov function, plus sensitivity bounds and local ISS linking spectral gap to robustness.
citing papers explorer
-
Markov Chains and Random Walks with Memory on Hypergraphs: A Tensor-Based Approach
A unified tensor framework models higher-order Markov chains with memory via an even-order paired tensor linking folded and unfolded dynamics, with approximation to low-dimensional nonlinear systems and application to hypergraph random walks.
-
Stability and Robustness of Tensor-Coupled Flow-Conservation Dynamical Systems on Hypergraphs
Under a tensor generalized detailed-balance condition, tensor-coupled flow-conservation systems on hypergraphs have a unique equilibrium with global asymptotic stability via an entropy Lyapunov function, plus sensitivity bounds and local ISS linking spectral gap to robustness.