Toward a computational history of science: The dynamics of socio-epistemic networks and the renaissance of general relativity

The exploding amount of available historical data provides intriguing possibilities as well as major challenges to historians of science. In the last years, several quantitative methods have been developed in order to analyze historical data. At the same time, new analytical frameworks need to be developed to bring together quantitative methods with the more traditional historians’ toolkit. The present paper has a twofold aim. The first one is to briefly review major quantitative approaches that have been developed in the history of science in two areas: data modeling and network analysis. The second part of the contribution focuses on applications of social network analysis to the evolution of knowledge systems. We propose a methodological and conceptual framework aiming at uncovering the dynamical transformations of intra- and inter-connections within and between different layers of the scientific enterprise. We define knowledge networks as being composed of three different layers: the social network, the semiotic network, and the semantic network. The first is defined as the collection of relations involving individuals and institutions. The semiotic network is defined as the collection of the material or formal representations of knowledge. The semantic network is the collection of knowledge elements and their relations. We call socio-epistemic networks the interlinked set of these three levels. As an illustration of this methodology results drawn from our own work on social and conceptual changes in the history of general relativity in the 20th century will be presented.