Static and Dynamic Aspects of Scientific Collaboration Networks

TL;DR

This paper analyzes scientific collaboration networks using graph methods, examining their structure, community detection, and the influence of academic events like Dagstuhl Seminars on network evolution and researcher behavior.

Contribution

It combines DBLP data with Dagstuhl Seminar data to study the impact of academic events on collaboration networks, a novel approach in this context.

Findings

Seminars do not significantly alter network structure.

Network structure influences researchers' seminar participation decisions.

Standard network properties and community detection methods are validated.

Abstract

Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based methods. We review standard properties of the network and rank authors and publications by centrality. Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. In a second part, we are the first to combine DBLP network data with data from the Dagstuhl Seminars: We investigate whether seminars of this kind, as social and academic events designed to connect researchers, leave a visible track in the structure of the collaboration network. Our results suggest that such single events are not influential enough to change the network structure significantly. However, the network structure seems to influence a participant's decision to accept or decline an invitation.