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用于刻画时变网络上传播动力学的保终身参考模型。

Lifetime-preserving reference models for characterizing spreading dynamics on temporal networks.

机构信息

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

出版信息

Sci Rep. 2018 Jan 15;8(1):709. doi: 10.1038/s41598-017-18450-3.

DOI:10.1038/s41598-017-18450-3
PMID:29335422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5768694/
Abstract

To study how a certain network feature affects processes occurring on a temporal network, one often compares properties of the original network against those of a randomized reference model that lacks the feature in question. The randomly permuted times (PT) reference model is widely used to probe how temporal features affect spreading dynamics on temporal networks. However, PT implicitly assumes that edges and nodes are continuously active during the network sampling period - an assumption that does not always hold in real networks. We systematically analyze a recently-proposed restriction of PT that preserves node lifetimes (PTN), and a similar restriction (PTE) that also preserves edge lifetimes. We use PT, PTN, and PTE to characterize spreading dynamics on (i) synthetic networks with heterogeneous edge lifespans and tunable burstiness, and (ii) four real-world networks, including two in which nodes enter and leave the network dynamically. We find that predictions of spreading speed can change considerably with the choice of reference model. Moreover, the degree of disparity in the predictions reflects the extent of node/edge turnover, highlighting the importance of using lifetime-preserving reference models when nodes or edges are not continuously present in the network.

摘要

为了研究特定网络特征如何影响时变网络上发生的过程,人们通常会将原始网络的属性与缺乏所研究特征的随机参考模型的属性进行比较。随机置换时间 (PT) 参考模型被广泛用于探测时变特征如何影响时变网络上的传播动力学。然而,PT 隐含地假设边和节点在网络采样期间是连续活动的——这一假设并不总是适用于真实网络。我们系统地分析了最近提出的保留节点寿命的 PT 限制 (PTN),以及一个类似的保留边寿命的限制 (PTE)。我们使用 PT、PTN 和 PTE 来描述(i)具有异质边寿命和可调突发的合成网络,以及(ii)包括两个节点动态进出网络的两个真实网络上的传播动力学。我们发现,参考模型的选择会导致传播速度的预测发生很大变化。此外,预测结果的差异程度反映了节点/边转换的程度,突出了当节点或边不在网络中连续存在时使用保留寿命的参考模型的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/e3b2798c484b/41598_2017_18450_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/92de466c9a0d/41598_2017_18450_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/4c93e0f05fbe/41598_2017_18450_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/c56805309c4d/41598_2017_18450_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/47da24dd1b73/41598_2017_18450_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/aac4dd7ce9cb/41598_2017_18450_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/e3b2798c484b/41598_2017_18450_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/92de466c9a0d/41598_2017_18450_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/4c93e0f05fbe/41598_2017_18450_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/c56805309c4d/41598_2017_18450_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/47da24dd1b73/41598_2017_18450_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/aac4dd7ce9cb/41598_2017_18450_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9cc4/5768694/e3b2798c484b/41598_2017_18450_Fig6_HTML.jpg

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3
Learning-induced autonomy of sensorimotor systems.学习诱导的感觉运动系统自主性
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PLoS Comput Biol. 2019 May 21;15(5):e1006530. doi: 10.1371/journal.pcbi.1006530. eCollection 2019 May.
Nat Neurosci. 2015 May;18(5):744-51. doi: 10.1038/nn.3993. Epub 2015 Apr 6.
4
Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks.因果驱动的非马尔可夫时变网络中扩散的慢化和加速。
Nat Commun. 2014 Sep 24;5:5024. doi: 10.1038/ncomms6024.
5
Contact patterns among high school students.高中生之间的接触模式。
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7
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8
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9
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10
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