Suppr超能文献

基于有限元分析的多维信息网络大数据挖掘算法。

Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis.

机构信息

Dalian Jiaotong University, Dalian, Liaoning 116028, China.

出版信息

Comput Intell Neurosci. 2022 Apr 11;2022:7156715. doi: 10.1155/2022/7156715. eCollection 2022.

Abstract

In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.

摘要

近年来,随着互联网的飞速发展,在线社交网络不断与传统人际关系网络融合,社交网络中的信息传播研究也逐渐增多。本文基于有限元分析方法研究和分析了多维信息网络大数据挖掘算法。本文首先介绍了有限元分析和计算过程,有限元数据挖掘模拟应用软件管理系统将把当前的数据、计算和背景数据整合为一体,然后分析了数据挖掘聚类算法,并对复杂网络中的影响节点挖掘算法进行了实验探索。实验结果表明,LIC 算法优于 CC 算法、DC 算法和 BC 算法;其整体性能提高了 30%,效果更好。LIC 算法可以有效地快速确定有影响力的节点,这有助于社交网络分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d66/9017521/5137e6192077/CIN2022-7156715.001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验