Combining Advanced Visualization and Automatized Reasoning for Webometrics: A Test Study

TL;DR

This study employs advanced visualization and automated reasoning techniques to analyze web-based communication among German computer science institutions, revealing detailed linking behaviors through multi-viewpoint data analysis.

Contribution

It introduces a novel application of MultiSOM clustering for automatic identification of linking behaviors in webometric analysis within a specific scientific domain.

Findings

Revealed global linking patterns among institutions.

Identified local variations in linking behavior.

Demonstrated effectiveness of MVDA in webometric studies.

Abstract

This paper presents a first attempt at performing a precise and automatic identification of the linking behaviour in a scientific domain through the analysis of the communication of the related academic institutions on the web. The proposed approach is based on the paradigm of multiple viewpoint data analysis (MVDA) than can be fruitfully exploited to highlight relationships between data, like websites, carrying several kinds of description. It uses the MultiSOM clustering and mapping method. The domain that has been chosen for this study is the domain of Computer Science in Germany. The analysis is conduced on a set of 438 websites of this domain using all together, thematic, geographic and linking information. It highlights interesting results concerning both global and local linking behaviour.