Dynamic Network of Concepts from Web-Publications

TL;DR

This paper presents a method for constructing and analyzing a dynamic network of concepts derived from web-publications, focusing on its structure stability over time as publication volume increases.

Contribution

It introduces an algorithm for extracting concepts and models the network's dynamic evolution based on co-occurrence in web documents.

Findings

Network structure remains stable as the number of publications grows.

Concept co-occurrence frequency effectively indicates relationship strength.

The dynamic behavior of the network provides insights into information flow patterns.

Abstract

The network, the nodes of which are concepts (people's names, companies' names, etc.), extracted from web-publications, is considered. A working algorithm of extracting such concepts is presented. Edges of the network under consideration refer to the reference frequency which depends on the fact how many times the concepts, which correspond to the nodes, are mentioned in the same documents. Web-documents being published within a period of time together form an information flow, which defines the dynamics of the network studied. The phenomenon of its structure stability, when the number of web-publications, constituting its formation bases, increases, is discussed