• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

演化三方网络中节点特征的分布

Distribution of Node Characteristics in Evolving Tripartite Network.

作者信息

Beranek Ladislav, Remes Radim

机构信息

Department of Applied Mathematics and Informatics, Faculty of Economics, University of South Bohemia, 37005 Ceske Budejovice, Czech Republic.

出版信息

Entropy (Basel). 2020 Feb 25;22(3):263. doi: 10.3390/e22030263.

DOI:10.3390/e22030263
PMID:33286037
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7516714/
Abstract

Many real-world networks have a natural tripartite structure. Investigating the structure and the behavior of actors in these networks is useful to gain a deeper understanding of their behavior and dynamics. In our paper, we describe an evolving tripartite network using a network model with preferential growth mechanisms and different rules for changing the strength of nodes and the weights of edges. We analyze the characteristics of the strength distribution and behavior of selected nodes and selected actors in this tripartite network. The distributions of these analyzed characteristics follow the power-law under different modeled conditions. Performed simulations have confirmed all these results. Despite its simplicity, the model expresses well the basic properties of the modeled network. It can provide further insights into the behavior of systems with more complex behaviors, such as the multi-actor e-commerce system that we have used as a real basis for the validation of our model.

摘要

许多现实世界的网络具有天然的三方结构。研究这些网络中参与者的结构和行为,有助于更深入地理解其行为和动态。在我们的论文中,我们使用一个具有优先增长机制以及改变节点强度和边权重的不同规则的网络模型,来描述一个演化的三方网络。我们分析了这个三方网络中选定节点和选定参与者的强度分布特征及行为。在不同的建模条件下,这些分析特征的分布遵循幂律。所进行的模拟证实了所有这些结果。尽管该模型很简单,但它很好地表达了所建模网络的基本属性。它可以为具有更复杂行为的系统的行为提供进一步的见解,比如我们用作模型验证实际基础的多参与者电子商务系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/f012607a2b1f/entropy-22-00263-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/b4e348d3eab4/entropy-22-00263-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/a12342808946/entropy-22-00263-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/623291346803/entropy-22-00263-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/f2547f2c2640/entropy-22-00263-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/d8be4060581a/entropy-22-00263-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/590ee39ba6f6/entropy-22-00263-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/f012607a2b1f/entropy-22-00263-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/b4e348d3eab4/entropy-22-00263-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/a12342808946/entropy-22-00263-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/623291346803/entropy-22-00263-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/f2547f2c2640/entropy-22-00263-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/d8be4060581a/entropy-22-00263-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/590ee39ba6f6/entropy-22-00263-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2de9/7516714/f012607a2b1f/entropy-22-00263-g006.jpg

相似文献

1
Distribution of Node Characteristics in Evolving Tripartite Network.演化三方网络中节点特征的分布
Entropy (Basel). 2020 Feb 25;22(3):263. doi: 10.3390/e22030263.
2
Influence of network properties on a migration induced secular height trend by Monte Carlo simulation.通过蒙特卡罗模拟研究网络属性对迁移引起的长期身高趋势的影响。
Anthropol Anz. 2019 Nov 8;76(5):433-443. doi: 10.1127/anthranz/2019/1032.
3
Investigating the Influence of Inverse Preferential Attachment on Network Development.研究反向优先连接对网络发展的影响。
Entropy (Basel). 2020 Sep 15;22(9):1029. doi: 10.3390/e22091029.
4
Power-law distribution of degree-degree distance: A better representation of the scale-free property of complex networks.度-度距离的幂律分布:复杂网络无标度特性的更好表示。
Proc Natl Acad Sci U S A. 2020 Jun 30;117(26):14812-14818. doi: 10.1073/pnas.1918901117. Epub 2020 Jun 15.
5
Self-organization of complex networks as a dynamical system.作为动态系统的复杂网络的自组织
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jan;91(1):012908. doi: 10.1103/PhysRevE.91.012908. Epub 2015 Jan 12.
6
Scaling properties of scale-free evolving networks: continuous approach.无标度演化网络的标度性质:连续方法
Phys Rev E Stat Nonlin Soft Matter Phys. 2001 May;63(5 Pt 2):056125. doi: 10.1103/PhysRevE.63.056125. Epub 2001 Apr 26.
7
Structure of shells in complex networks.复杂网络中壳层的结构
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Sep;80(3 Pt 2):036105. doi: 10.1103/PhysRevE.80.036105. Epub 2009 Sep 9.
8
Resilience of supply-chain systems under perturbations: A network approach.扰动下供应链系统的弹性:一种网络方法。
Chaos. 2022 Sep;32(9):093123. doi: 10.1063/5.0096983.
9
Understanding the implementation of evidence-based care: a structural network approach.理解循证护理的实施:一种结构网络方法。
Implement Sci. 2011 Feb 24;6:14. doi: 10.1186/1748-5908-6-14.
10
The Analysis of the Power Law Feature in Complex Networks.复杂网络中幂律特征的分析
Entropy (Basel). 2022 Oct 29;24(11):1561. doi: 10.3390/e24111561.

本文引用的文献

1
Link Prediction in Bipartite Nested Networks.二分嵌套网络中的链接预测
Entropy (Basel). 2018 Oct 10;20(10):777. doi: 10.3390/e20100777.
2
Analysis of Basic Features in Dynamic Network Models.动态网络模型的基本特征分析
Entropy (Basel). 2018 Sep 7;20(9):681. doi: 10.3390/e20090681.
3
Robustness of network attack strategies against node sampling and link errors.网络攻击策略对节点采样和链路错误的鲁棒性。
PLoS One. 2019 Sep 4;14(9):e0221885. doi: 10.1371/journal.pone.0221885. eCollection 2019.
4
The ecology of movement and behaviour: a saturated tripartite network for describing animal contacts.运动与行为生态学:描述动物接触的饱和三分网络。
Proc Biol Sci. 2018 Sep 19;285(1887):20180670. doi: 10.1098/rspb.2018.0670.
5
Detection of statistically significant network changes in complex biological networks.复杂生物网络中具有统计学意义的网络变化检测。
BMC Syst Biol. 2017 Mar 4;11(1):32. doi: 10.1186/s12918-017-0412-6.
6
The evolving cobweb of relations among partially rational investors.部分理性投资者之间不断演变的关系网络。
PLoS One. 2017 Feb 14;12(2):e0171891. doi: 10.1371/journal.pone.0171891. eCollection 2017.
7
Modelling the evolution of a bi-partite network Peer referral in interlocking directorates.双分网络中同伴推荐在连锁董事会中的演变建模。
Soc Networks. 2012 Jul 1;34(3):309-322. doi: 10.1016/j.socnet.2010.03.001.
8
The emerging paradigm of network medicine in the study of human disease.网络医学在人类疾病研究中新兴的范式。
Circ Res. 2012 Jul 20;111(3):359-74. doi: 10.1161/CIRCRESAHA.111.258541.
9
Network medicine: a network-based approach to human disease.网络医学:一种基于网络的人类疾病研究方法。
Nat Rev Genet. 2011 Jan;12(1):56-68. doi: 10.1038/nrg2918.
10
A simple model of bipartite cooperation for ecological and organizational networks.生态与组织网络的二分合作简单模型。
Nature. 2009 Jan 22;457(7228):463-6. doi: 10.1038/nature07532. Epub 2008 Dec 3.