Proximity Matters: Analyzing the Role of Geographical Proximity in Shaping AI Research Collaborations

TL;DR

This study investigates how geographical proximity influences individual-level scientific collaborations in AI, revealing that distance hampers collaboration but network proximity can compensate for geographical gaps.

Contribution

It provides empirical evidence on the impact of geographical and network proximities on AI research collaborations at the individual researcher level.

Findings

Geographical distance impedes individual scientific collaboration.

Network proximity increases collaboration likelihood over long distances.

Network proximity can substitute for geographical proximity in fostering collaborations.

Abstract

The role of geographical proximity in facilitating inter-regional or inter-organizational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level still needs to be addressed. Using publication data in the field of artificial intelligence from 2001 to 2019, in this work, the effect of geographical proximity on the likelihood of forming future scientific collaborations among researchers is studied. In addition, the interaction between geographical and network proximities is examined to see whether network proximity can substitute geographical proximity in encouraging long-distance scientific collaborations. Employing conventional and machine learning techniques, our results suggest that geographical distance impedes scientific collaboration at the individual level despite the tremendous improvements in transportation and communication technologies during recent decades. Moreover, our findings show that the effect of network proximity on the likelihood of scientific collaboration increases with geographical distance, implying that network proximity can act as a substitute for geographical proximity.