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+ (1−β)P(X t−1 ξ = 1,X s ξ = 1)− αξ αξ +βξ 2 =αξP(Xs ξ = 1)−α ξE(Xt−1 ξ Xs ξ ) + (1−β ξ)E(Xt−1 ξ Xs ξ )− αξ αξ +βξ 2 =(1−α ξ−βξ) E Xt−1 ξ Xs ξ −E Xt−1 ξ E Xs ξ =(1−α ξ−βξ) Cov Xt−1 ξ ,X s ξ So we have ρξ,ξ′(|t−s|) = (1−α ξ−βξ)|t−s| forξ=ξ ′ · 2009

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stat.ME 1

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2025 1

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Autoregressive Hypergraph

stat.ME · 2025-06-20 · conditional · novelty 6.0

A first-order autoregressive model for temporal non-uniform hypergraphs is introduced, with maximum-likelihood inference, a transition-probability Laplacian for spectral community detection, and a likelihood-based change-point estimator.

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  • Autoregressive Hypergraph stat.ME · 2025-06-20 · conditional · none · ref 1

    A first-order autoregressive model for temporal non-uniform hypergraphs is introduced, with maximum-likelihood inference, a transition-probability Laplacian for spectral community detection, and a likelihood-based change-point estimator.