Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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2026 5verdicts
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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.
For Markov sources, redaction up to strong stationary times achieves perfect privacy with optimal utility using constant average redactions independent of length.
The paper derives sufficient conditions for consensus and convergence rates in multiplex networks using merged and switching coupling models for coordination games, showing that interlayer interactions can induce or disrupt global agreement depending on layer similarity.
The paper develops independent and joint scheduling policies for collaborative ISAC that maximize discriminant gain for classification while respecting energy and eMBB constraints, outperforming baselines especially when devices are correlated.
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Decentralized Learning via Random Walk with Jumps
Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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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.
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Perfect Privacy and Strong Stationary Times for Markovian Sources
For Markov sources, redaction up to strong stationary times achieves perfect privacy with optimal utility using constant average redactions independent of length.
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Coordination Games on Multiplex Networks: Consensus, Convergence, and Stability of Opinion Dynamics
The paper derives sufficient conditions for consensus and convergence rates in multiplex networks using merged and switching coupling models for coordination games, showing that interlayer interactions can induce or disrupt global agreement depending on layer similarity.
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Goal-oriented Resource Allocation for Collaborative Integrated Sensing and Communication
The paper develops independent and joint scheduling policies for collaborative ISAC that maximize discriminant gain for classification while respecting energy and eMBB constraints, outperforming baselines especially when devices are correlated.