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时间网络中的相关爆发会减缓传播。

Correlated bursts in temporal networks slow down spreading.

作者信息

Hiraoka Takayuki, Jo Hang-Hyun

机构信息

Asia Pacific Center for Theoretical Physics, Pohang, 37673, Republic of Korea.

Department of Physics, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.

出版信息

Sci Rep. 2018 Oct 17;8(1):15321. doi: 10.1038/s41598-018-33700-8.

DOI:10.1038/s41598-018-33700-8
PMID:30333572
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6193034/
Abstract

Spreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results.

摘要

传播动力学被认为发生在时间网络中，其中节点之间的时间交互模式呈现非泊松式的突发性质。近年来，不均匀事件间隔时间（IET）对传播的影响已得到广泛研究，但对于IET之间的相关性对传播的影响却知之甚少。为了研究这些影响，当交互模式由不均匀且相关的IET（即相关突发）建模时，我们研究了两步确定性易感-感染（SI）和概率性SI动力学。通过分析单链路设置中的传输时间统计数据，并通过在贝叶斯晶格和随机图中模拟传播，我们得出结论：IET之间的正相关性会减缓传播。我们还认为，从一个受感染节点到其易感邻居的最短传输时间能够成功解释我们的数值结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/888c8e3d056b/41598_2018_33700_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/8ba8730e6dad/41598_2018_33700_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/3b183364d768/41598_2018_33700_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/b2ede5132248/41598_2018_33700_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/d698ffbd31c8/41598_2018_33700_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/b4f54cd41088/41598_2018_33700_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/888c8e3d056b/41598_2018_33700_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/8ba8730e6dad/41598_2018_33700_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/3b183364d768/41598_2018_33700_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/b2ede5132248/41598_2018_33700_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/d698ffbd31c8/41598_2018_33700_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/b4f54cd41088/41598_2018_33700_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1f3/6193034/888c8e3d056b/41598_2018_33700_Fig6_HTML.jpg

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本文引用的文献

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Limits of the memory coefficient in measuring correlated bursts.测量相关爆发时记忆系数的局限性。
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2
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Phys Rev E. 2017 Dec;96(6-1):062131. doi: 10.1103/PhysRevE.96.062131. Epub 2017 Dec 18.
3
Bounds of memory strength for power-law series.幂律级数记忆强度的边界。
Sci Rep. 2020 Jul 22;10(1):12202. doi: 10.1038/s41598-020-68157-1.
4
Concurrency and reachability in treelike temporal networks.树状时间网络中的并发性和可达性。
Phys Rev E. 2019 Dec;100(6-1):062305. doi: 10.1103/PhysRevE.100.062305.
Phys Rev E. 2017 May;95(5-1):052314. doi: 10.1103/PhysRevE.95.052314. Epub 2017 May 19.
4
Temporal dynamics of online petitions.在线请愿的时间动态。
PLoS One. 2017 May 18;12(5):e0178062. doi: 10.1371/journal.pone.0178062. eCollection 2017.
5
Dynamics on networks: competition of temporal and topological correlations.网络动力学：时间和拓扑关联的竞争。
Sci Rep. 2017 Feb 2;7:41627. doi: 10.1038/srep41627.
6
Emergence of long-range correlations and bursty activity patterns in online communication.在线交流中长程相关性和突发活动模式的出现。
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062821. doi: 10.1103/PhysRevE.92.062821. Epub 2015 Dec 17.
7
Diffusion on networked systems is a question of time or structure.网络系统中的扩散是一个时间或结构的问题。
Nat Commun. 2015 Jun 9;6:7366. doi: 10.1038/ncomms8366.
8
Generalized epidemic process on modular networks.模块化网络上的广义流行过程。
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052811. doi: 10.1103/PhysRevE.89.052811. Epub 2014 May 21.
9
Measuring large-scale social networks with high resolution.以高分辨率测量大规模社会网络。
PLoS One. 2014 Apr 25;9(4):e95978. doi: 10.1371/journal.pone.0095978. eCollection 2014.
10
Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics.突发式通信模式有利于基于阈值的传染病动力学中的传播。
PLoS One. 2013 Jul 19;8(7):e68629. doi: 10.1371/journal.pone.0068629. Print 2013.