Predictability of real temporal networks

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.

References in corpus (12)

• Temporal Networks
• Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
• Hierarchical structure and the prediction of missing links in networks
• Quantifying Long-Term Scientific Impact
• A dynamic network approach for the study of human phenotypes
• The fundamental advantages of temporal networks
• Method for Generating Long-Range Correlations for Large Systems
• Impact of non-Poisson activity patterns on spreading processes
• Causality-Driven Slow-Down and Speed-Up of Diffusion in Non-Markovian Temporal Networks
• Temporal networks: slowing down diffusion by long lasting interactions
• Modeling sequences and temporal networks with dynamic community structures
• Compensating for population sampling in simulations of epidemic spread on temporal contact networks

Cited by in corpus (5)

• Progresses and Challenges in Link Prediction
• Efficient network immunization under limited knowledge
• The maximum capability of a topological feature in link prediction
• Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media
• A novel similarity measure for mining missing links in long-path networks