A new algorithm, t-NeSt, samples random temporal networks that preserve the time-respecting (causal) neighborhood structure up to a chosen depth d, with theoretical guarantees for temporal Katz centrality.
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Efficient Sampling of Temporal Networks with Preserved Causality Structure
A new algorithm, t-NeSt, samples random temporal networks that preserve the time-respecting (causal) neighborhood structure up to a chosen depth d, with theoretical guarantees for temporal Katz centrality.