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 evidence has shown that the temporal nature of links in many real-world networ...
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| Vydané v: | National science review Ročník 7; číslo 5; s. 929 - 937 |
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| Jazyk: | English |
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Oxford University Press
01.05.2020
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| ISSN: | 2095-5138, 2053-714X, 2053-714X |
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| Abstract | 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.
An entropy-based framework solved the long standing problem of quantifying the regularities in any temporal network whose links change over time, and unraveled the limit of predictability for 18 real temporal networks in diverse fields. |
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| AbstractList | 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. An entropy-based framework solved the long standing problem of quantifying the regularities in any temporal network whose links change over time, and unraveled the limit of predictability for 18 real temporal networks in diverse fields. 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.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. 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. An entropy-based framework solved the long standing problem of quantifying the regularities in any temporal network whose links change over time, and unraveled the limit of predictability for 18 real temporal networks in diverse fields. 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. |
| Author | Du, Wenbo Shekhtman, Louis Havlin, Shlomo Cao, Xianbin Tang, Disheng Wang, Yijie Yan, Gang |
| AuthorAffiliation | 5 Department of Physics, Bar Ilan University , Ramat Gan 5290002, Israel 1 School of Electronic and Information Engineering, Beihang University , Beijing 100191, China 7 CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences , Shanghai 200031, China 2 School of Physics Science and Engineering, Tongji University , Shanghai 200092, China 4 Network Science Institute, Northeastern University , Boston, MA 02115, USA 6 Shanghai Institute of Intelligence Science and Technology, Tongji University , Shanghai 200092, China 3 National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation , Beijing 100191, China |
| AuthorAffiliation_xml | – name: 3 National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation , Beijing 100191, China – name: 7 CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences , Shanghai 200031, China – name: 6 Shanghai Institute of Intelligence Science and Technology, Tongji University , Shanghai 200092, China – name: 1 School of Electronic and Information Engineering, Beihang University , Beijing 100191, China – name: 4 Network Science Institute, Northeastern University , Boston, MA 02115, USA – name: 2 School of Physics Science and Engineering, Tongji University , Shanghai 200092, China – name: 5 Department of Physics, Bar Ilan University , Ramat Gan 5290002, Israel |
| Author_xml | – sequence: 1 givenname: Disheng surname: Tang fullname: Tang, Disheng organization: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China – sequence: 2 givenname: Wenbo surname: Du fullname: Du, Wenbo email: xbcao@buaa.edu.cn organization: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China – sequence: 3 givenname: Louis surname: Shekhtman fullname: Shekhtman, Louis organization: Network Science Institute, Northeastern University, Boston, MA 02115, USA – sequence: 4 givenname: Yijie surname: Wang fullname: Wang, Yijie organization: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China – sequence: 5 givenname: Shlomo surname: Havlin fullname: Havlin, Shlomo organization: Department of Physics, Bar Ilan University, Ramat Gan 5290002, Israel – sequence: 6 givenname: Xianbin surname: Cao fullname: Cao, Xianbin email: xbcao@buaa.edu.cn organization: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China – sequence: 7 givenname: Gang orcidid: 0000-0001-6196-2615 surname: Yan fullname: Yan, Gang email: gyan@tongji.edu.cn organization: School of Physics Science and Engineering, Tongji University, Shanghai 200092, China |
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Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has... 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... |
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