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

Area of Science:

• Computational Social Science
• Network Analysis
• Data Mining

Background:

• The proliferation of the internet has led to the integration of online social networks with traditional interpersonal networks.
• Research on information dissemination within social networks is increasingly important.

Purpose of the Study:

• To analyze multidimensional information network big data mining using the finite element analysis method.
• To develop and evaluate an algorithm for identifying influential nodes in complex networks.

Main Methods:

• Introduction to the finite element analysis and calculation process.
• Development of a finite element data mining simulation application software management system.
• Analysis of data mining clustering algorithms and experimental exploration of influential node mining algorithms.

Main Results:

• The proposed LIC algorithm demonstrated superior performance compared to CC, DC, and BC algorithms.
• Overall performance improvement of the LIC algorithm reached 30% over existing methods.
• The LIC algorithm effectively and rapidly identifies influential nodes in complex networks.

Conclusions:

• The LIC algorithm is a highly effective tool for identifying influential nodes in social network analysis.
• Finite element analysis provides a robust framework for big data mining in multidimensional information networks.
• The findings contribute to a better understanding of information dissemination in complex networks.