ArticleNational science review2020
Predictability of real temporal networks.
Article in National science review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Beyond network centrality: individual-level behavioral traits for predicting information superspreaders in social media.National science review · 2024Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- Modulating the dynamics of NFκB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response.NPJ systems biology and applications · 2023Article
- Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links.Entropy (Basel, Switzerland) · 2023Review
- Progresses and challenges in link prediction.iScience · 2021Review
- Dynamic activity chain pattern estimation under mobility demand changes during COVID-19.Transportation research. Part C, Emerging technologies · 2021Article
- Coordinating directional switches in pigeon flocks: the role of nonlinear interactions.Royal Society open science · 2021Article
- Efficient network immunization under limited knowledge.National science review · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.