  Links in most real networks often change over time. Such temporality of links
encodes the ordering and causality of interactions between nodes and has a
profound effect on network dynamics and function. Empirical evidences have
shown that the temporal nature of links in many real-world networks is not
random. Nonetheless, it is challenging to predict temporal link patterns while
considering the entanglement between topological and temporal link patterns.
Here we propose an entropy-rate based framework, based on combined
topological-temporal regularities, for quantifying the predictability of any
temporal network. We apply our framework on various model networks,
demonstrating that it indeed captures the intrinsic topological-temporal
regularities whereas previous methods considered only temporal aspects. We also
apply our framework on 18 real networks of different types and determine their
predictability. Interestingly, we find that for most real temporal networks,
despite the greater complexity of predictability brought by the increase in
dimension the combined topological-temporal predictability is higher than the
temporal predictability. Our results demonstrate the necessity of incorporating
both temporal and topological aspects of networks in order to improve
predictions of dynamical processes.
