Cross-Relation Characterization of Knowledge Networks

TL;DR

This paper analyzes the evolution of knowledge networks in Physics and Theology from Wikipedia over four years, using cross-relation signatures and modification indices to compare real networks with theoretical models.

Contribution

It introduces a methodology for characterizing knowledge network changes over time and applies it to real-world Wikipedia data, comparing with ER and BA models.

Findings

Physics network resembles Barabasi-Albert model

Theology network resembles Erdős-Rényi model

Higher modification indices observed in periphery nodes

Abstract

Knowledge networks have become increasingly important as a changing repository of data which can be represented, studied and modeled by using complex networks concepts and methodologies. Here we report a study of knowledge networks corresponding to the areas of Physics and Theology, obtained from the Wikipedia and taken at two different dates separated by 4 years. The respective two versions of these networks were characterized in terms of their respective cross-relation signatures, being summarized in terms of modification indices obtained for each of the nodes that are preserved among the two versions. The proposed methodology is first evaluated on Erdos-Renyi (ER) and Barabasi-Albert model (BA) networks, before being tested on the knowledge networks obtained from the Wikipedia respectively to the areas of Physics and Theology. In the former study, it has been observed that the nodes at the core and periphery of both types of theoretical models yielded similar modification indices within these two groups of nodes, but with distinct values when taken across these two groups. The study of the Physics and Theology networks indicated that these two networks have signatures respectively similar to those of the BA and ER models, as well as that higher modification values being obtained for the periphery nodes, as compared to the respective core nodes.