The temporarily of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function, however it is challenging to predict temporal link patterns. In order to alter network dynamical states in a desirable way, it is essential to quantitatively understand both topological and temporal patterns. Scientists based in China and Israel proposed an entropy-rate based framework of predictability that captures the combined topological-temporal regularities in any temporal network.
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