Abstract
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 evidence has 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 for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.
Highlights
Link temporality describes the time-varying nature of couplings and interactions between nodes in real networks [1,2,3,4,5,6,7,8,9,10,11,12], which has been found to significantly affect network dynamics
Markov considers a temporal network as a set of uncorrelated time series, ConvLSTM takes into consideration link correlations, and Predictive Coding Network (PredNet) is a dynamic matrix-prediction algorithm based on ConvLSTM
We developed a 2D framework, based on combined topology–temporal features, for quantifying the intrinsic predictability and uncovering the predictability profile of any temporal network
Summary
Link temporality describes the time-varying nature of couplings and interactions between nodes in real networks [1,2,3,4,5,6,7,8,9,10,11,12], which has been found to significantly affect network dynamics. We sort the rows, i.e. all potential links, in descending order according to the number of their occurrences in all snapshots and remove those links that are present in
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