What Can Heterogeneity Add to the Scientometric Map? Steps towards algorithmic historiography

TL;DR

This paper explores how incorporating heterogeneity into scientometric maps enhances network visualization, using Michel Callon's publication history as a case study to demonstrate improvements over traditional one-dimensional maps.

Contribution

It investigates the added value of heterogeneity in network visualization, proposing steps towards more nuanced algorithmic historiography.

Findings

Heterogeneity enriches network visualization by representing diverse entities.

Integrated networks provide deeper insights than one-dimensional maps.

Case study of Michel Callon's oeuvre illustrates these benefits.

Abstract

The Actor Network represents heterogeneous entities as actants (Callon et al., 1983; 1986). Although computer programs for the visualization of social networks increasingly allow us to represent heterogeneity in a network using different shapes and colors for the visualization, hitherto this possibility has scarcely been exploited (Mogoutov et al., 2008). In this contribution to the Festschrift, I study the question of what heterogeneity can add specifically to the visualization of a network. How does an integrated network improve on the one-dimensional ones (such as co-word and co-author maps)? The oeuvre of Michel Callon is used as the case materials, that is, his 65 papers which can be retrieved from the (Social) Science Citation Index since 1975.