信息迭代传播对复杂网络结构的影响研究。

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2024-07-27 DOI:10.1089/big.2023.0016

Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao

{"title":"信息迭代传播对复杂网络结构的影响研究。","authors":"Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao","doi":"10.1089/big.2023.0016","DOIUrl":null,"url":null,"abstract":"Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.","PeriodicalId":51314,"journal":{"name":"Big Data","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on the Influence of Information Iterative Propagation on Complex Network Structure.\",\"authors\":\"Yinuo Qian, Fuzhong Nian, Zheming Wang, Yabing Yao\",\"doi\":\"10.1089/big.2023.0016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.\",\"PeriodicalId\":51314,\"journal\":{\"name\":\"Big Data\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1089/big.2023.0016\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1089/big.2023.0016","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

动态传播会影响网络结构的变化。不同的网络受信息迭代传播的影响程度不同。网络中信息的迭代传播会改变节点间链边的连接强度。大多数关于时态网络的研究都是基于时间特征来构建网络的，网络中信息的迭代传播也能反映网络演化的时间特征。网络结构的变化是时间特征的宏观体现，而网络中的动态变化则是时间特征的微观体现。如何具体直观地体现传播动力学特征对网络结构变化的影响，成为本文讨论的重点。链边的出现是网络结构的微观变化，社区的划分是网络结构的宏观变化。在此基础上，提出了节点参与度来量化不同用户对网络信息传播的影响，并在不同类型的网络中进行了模拟。通过对信息迭代传播的分析，构建了基于信息迭代传播的不同网络的加权网络。最后，通过分析网络中的链边和社区划分，达到量化网络传播对复杂网络结构影响的目的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Research on the Influence of Information Iterative Propagation on Complex Network Structure.

Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.