Cross-Community Dynamics in Science: How Information Retrieval Affects Semantic Web and Vice Versa

TL;DR

This paper introduces a flexible framework for analyzing cross-community effects in scientific research, leveraging community algorithms and meta-data to better understand and explain the dynamics between related research communities.

Contribution

It proposes a novel, general methodology for analyzing and interpreting cross-community effects, enhancing the accuracy and depth of such analyses in scientific research.

Findings

Identified detailed cross-community effects between related research areas.

Demonstrated the framework's ability to interpret complex community dynamics.

Highlighted open issues for future research in community lifecycle prediction.

Abstract

Community effects on the behaviour of individuals, the community itself and other communities can be observed in a wide range of applications. This is true in scientific research, where communities of researchers have increasingly to justify their impact and progress to funding agencies. While previous work has tried to explain and analyse such phenomena, there is still a great potential for increasing the quality and accuracy of this analysis, especially in the context of cross-community effects. In this work, we propose a general framework consisting of several different techniques to analyse and explain such dynamics. The proposed methodology works with arbitrary community algorithms and incorporates meta-data to improve the overall quality and expressiveness of the analysis. We suggest and discuss several approaches to understand, interpret and explain particular phenomena, which themselves are identified in an automated manner. We illustrate the benefits and strengths of our approach by exposing highly interesting in-depth details of cross-community effects between two closely related and well established areas of scientific research. We finally conclude and highlight the important open issues on the way towards understanding, defining and eventually predicting typical life-cycles and classes of communities in the context of cross-community effects.