Dynamic networks can be modeled as decorated graphons whose edge labels are binary time-series laws, estimated by two-stage blockwise least squares with rates depending on the number of time steps and edge-estimator quality.
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Decorated graphons for temporal network estimation
Dynamic networks can be modeled as decorated graphons whose edge labels are binary time-series laws, estimated by two-stage blockwise least squares with rates depending on the number of time steps and edge-estimator quality.