GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science

TL;DR

This paper introduces GeoDBLP, a geo-tagged researcher database, revealing universal migration patterns across countries and modeling researcher migration as a Poisson process with career-stage-dependent propensities.

Contribution

It presents GeoDBLP, a large-scale geo-tagged researcher dataset, and models researcher migration patterns, uncovering universal regularities and statistical distributions.

Findings

Researcher migration follows a Poisson process with log-normal propensities.

Migration propensity varies with career stage, following a gamma distribution.

Universal patterns in researcher migration have implications for global education and research strategies.

Abstract

Many collective human activities have been shown to exhibit universal patterns. However, the possibility of universal patterns across timing events of researcher migration has barely been explored at global scale. Here, we show that timing events of migration within different countries exhibit remarkable similarities. Specifically, we look at the distribution governing the data of researcher migration inferred from the web. Compiling the data in itself represents a significant advance in the field of quantitative analysis of migration patterns. Official and commercial records are often access restricted, incompatible between countries, and especially not registered across researchers. Instead, we introduce GeoDBLP where we propagate geographical seed locations retrieved from the web across the DBLP database of 1,080,958 authors and 1,894,758 papers. But perhaps more important is that we are able to find statistical patterns and create models that explain the migration of researchers. For instance, we show that the science job market can be treated as a Poisson process with individual propensities to migrate following a log-normal distribution over the researcher's career stage. That is, although jobs enter the market constantly, researchers are generally not "memoryless" but have to care greatly about their next move. The propensity to make k>1 migrations, however, follows a gamma distribution suggesting that migration at later career stages is "memoryless". This aligns well but actually goes beyond scientometric models typically postulated based on small case studies. On a very large, transnational scale, we establish the first general regularities that should have major implications on strategies for education and research worldwide.