Publications

Author networks in science often rely on citation analyses. In such cases, as in others, network interpretation usually depends on supplementary data, notably about authors’ research domains when disciplinary interpretations are sought. More general social networks also face similar interpretation challenges as to the semantic content specificities of their members. In this research-in-progress, we propose to infer author networks not from citation analyses but from topic similarity analyses based on a topic-model of published documents. Such author networks reveal, as we call them, “hidden communities of interest” (HCoIs) whose semantic content can easily be interpreted by means of their associated topics in the model. We use an astrobiology corpus of full-text articles (N = 3,698) to illustrate the approach. Having conducted an LDA topic-model on all publications, we identify the underlying communities of authors by measuring author correlations in terms of topic distributions. Adding publication dates makes it possible to examine HCoI evolution over time. This approach to social networks supplements traditional methods in contexts where textual data are available.

Malaterre, C. et Lareau, F. (2024). Visualizing hidden communities of interest: A case-study analysis of topic-based social networks in astrobiology. Scientometrics.