Intelligent data analysis based on the complex network theory methods: a case study

TL;DR

This paper demonstrates how complex network theory methods can be integrated with statistical analysis and concept mapping to perform intelligent data analysis on scientific publication data, revealing hidden patterns and connections.

Contribution

It introduces a novel approach combining complex network theory with statistical and concept mapping techniques for intelligent data analysis in scientific data.

Findings

Identified underlying patterns in scientific publication data.

Revealed hidden connections within journal information.

Enhanced information retrieval capabilities.

Abstract

The development of modern information technologies permits to collect and to analyze huge amounts of statistical data in different spheres of life. The main problem is not to only to collect but to process all relevant information. The purpose of our work is to show the example of intelligent data analysis in such complex and non-formalized field as science. Using the statistical data about scientific periodical it is possible to perform its comprehensive analysis and to solve different practical problems. The combination of various approaches including the statistical analysis, methods of the complex network theory and different techniques that can be used for the concept mapping permits to perform an intelligent data analysis in order to obtain underlying patterns and hidden connections. Results of such analysis can be used for particular practical problems like information retrieval within journal.