Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2024-09-27 DOI:10.1016/j.bdr.2024.100493
Yinuo Qian, Fuzhong Nian
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Abstract

In order to simulate the forwarding situation of different categories of Weibo and discover interesting propagation phenomena in different layers of Weibo networks, this paper proposes the retweeting weighted multiplex networks and propagation model coupled with multi-class Weibo. Firstly, the weighted multiplex social network is constructed through the processing of Weibo network data. Secondly, a new information propagation model is established by using the weight and interlayer information of the Weibo multiplex network combined with the coupling factors in the propagation. Finally, the information propagation simulated by the propagation model is compared with the real data, so as to summarize different information propagation phenomena in multiplex social multiplex network. At the same time, by comparing the structure of the forwarding weighted multiplex network constructed by the short time data and the long time data, we find the self-similarity of the forwarding weighted multiplex network, which proves the generalization of the experiment. Through the above research, the mystery of the Weibo social network has been deeply explored, and a new perspective has been opened up for the exploration of social media information propagation.
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加权复用微博网络信息传播动态特征研究
为了模拟不同类别微博的转发情况,发现微博网络不同层级中有趣的传播现象,本文提出了与多类别微博耦合的转发加权复用网络及传播模型。首先,通过对微博网络数据的处理构建了加权复用社交网络。其次,利用微博复用网络的权重和层间信息,结合传播中的耦合因子,建立了新的信息传播模型。最后,将传播模型模拟的信息传播与真实数据进行对比,从而总结出复用社交复用网络中不同的信息传播现象。同时,通过对比短时间数据和长时间数据构建的转发加权复用网络结构,发现转发加权复用网络的自相似性,证明了实验的普适性。通过以上研究,深入探索了微博社交网络的奥秘,为探索社交媒体信息传播开辟了新的视角。
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来源期刊
Big Data Research
Big Data Research Computer Science-Computer Science Applications
CiteScore
8.40
自引率
3.00%
发文量
0
期刊介绍: The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
期刊最新文献
Incomplete data classification via positive approximation based rough subspaces ensemble Joint embedding in hierarchical distance and semantic representation learning for link prediction Deep semantics-preserving cross-modal hashing Research on the characteristics of information propagation dynamic on the weighted multiplex Weibo networks Leveraging social computing for epidemic surveillance: A case study
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