Know thy tools! Limits of popular algorithms used for topic reconstruction
To reconstruct topics in bibliometric networks, one must use algorithms. Specifically, researchers often apply algorithms from the class of network community detection algorithms (such as the Louvain algorithm) that are general-purpose algorithms not intentionally programmed for a bibliometric task. Each algorithm has specific properties “inscribed,” which distinguish it from the others. It can thus be assumed that different algorithms are more or less suitable for a given bibliometric task. However, the suitability of a specific algorithm when it is applied for topic reconstruction is rarely reflected upon. Why choose this algorithm and not another? In this study, I assess the suitability of four community detection algorithms for topic reconstruction, by first deriving the properties of the phenomenon to be reconstructed—topics—and comparing if these match with the properties of the algorithms. The results suggest that the previous use of these algorithms for bibliometric purposes cannot be justified by their specific suitability for this task.
Published in: Quantitative Science Studies, 10.1162/qss_a_00217, MIT Press