Predicting Scientific Success Based on Coauthorship Networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia

A Tuple Space for Social Networking on Mobile Phones

Abstract — Social networking is increasingly becoming a popular means of communication for online users. The trend is also true for offline scenarios where people use their mobile phones to network with nearby buddies. In this paper, we propose a distributed tuple space for social networking on ad hoc networks. We describe the tuple space model and its operations, and give evidence of its advantages for ad hoc social networking through several applications. I

Quantifying the effect of editor–author relations on manuscript handling times

In this article we study to what extent the academic peer review process is influenced by social relations between the authors of a manuscript and the editor handling the manuscript. Taking the open access journal PlosOne as a case study, our analysis is based on a data set of more than 100,000 articles published between 2007 and 2015. Using available data on handling editor, submission and acceptance time of manuscripts, we study the question whether co-authorship relations between authors and the handling editor affect the manuscript handling time, i.e. the time taken between the submission and acceptance of a manuscript. Our analysis reveals (1) that editors handle papers co-authored by previous collaborators significantly more often than expected at random, and (2) that such prior co-author relations are significantly related to faster manuscript handling. Addressing the question whether these shorter manuscript handling times can be explained by the quality of publications, we study the number of citations and downloads which accepted papers eventually accumulate. Moreover, we consider the influence of additional (social) factors, such as the editor’s experience, the topical similarity between authors and editors, as well as reciprocal citation relations between authors and editors. Our findings show that, even when correcting for other factors like time, experience, and performance, prior co-authorship relations have a large and significant influence on manuscript handling times, speeding up the editorial decision on average by 19 days