STRUCTURAL SIMILARITIES OF COMPLEX NETWORKS: A COMPUTATIONAL MODEL BY EXAMPLE OF WIKI GRAPHS

This article elaborates a framework for representing and classifying large complex networks by example of wiki graphs. By means of this framework we reliably measure the similarity of document, agent, and word networks by solely regarding their topology. In doing so, the article departs from classical approaches to complex network theory which focuses on topological characteristics in order to check their small world property. This does not only include characteristics that have been studied in complex network theory, but also some of those which were invented in social network analysis and hypertext theory. We show that network classifications come into reach which go beyond the hypertext structures traditionally analyzed in web mining. The reason is that we focus on networks as a whole as units to be classified—above the level of websites and their constitutive pages. As a consequence, we bridge classical approaches to text and web mining on the one hand and complex network theory on the other hand. Last but not least, this approach also provides a framework for quantifying the linguistic notion of intertextuality.

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