Predictability of real temporal networks

TL;DR

This paper introduces an entropy-rate based framework to quantify and analyze the predictability of real temporal networks by considering both topological and temporal link patterns, revealing higher predictability when combining these aspects.

Contribution

The paper presents a novel entropy-rate based framework that captures topological-temporal regularities in networks, improving predictability analysis over previous methods that only consider temporal aspects.

Findings

The framework effectively captures intrinsic topological-temporal regularities.

Predictability is higher when combining topological and temporal information.

Most real networks show greater predictability with combined analysis.

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 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.