Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications, and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed by either interacting with a fixed network data set or querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h -index and with ground truth data positioned in a data set of AMO physics illustrate the properties of MALBA and its potential.

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.

Often, bibliometric mapping studies remain at a very abstract level when assessing the validity or accuracy of the generated maps. In this case study of citation-based mappings of a research specialty, we dig deeper into the topical structures generated by the chosen mapping approaches and examine their correspondence to a sociologically informed understanding of the research specialty in question. Starting from a lexically delineated bibliometric field data set, we create an internal map of invasion biology by clustering the direct citation network with the Leiden algorithm. We obtain a topic structure that seems largely ordered by the empirical objects studied (species and habitat). To complement this view, we generate an external map of invasion biology by projecting the field data set onto the global Centre for Science and Technology Studies (CWTS) field classification. To better understand the representation of invasion biology by this global map, we use a manually coded set of invasion biological publications and investigate their citation-based interlinking with the fields defined by the global field classification. Our analysis highlights the variety of types of topical relatedness and epistemic interdependency that citations can stand for. Unless we assume that invasion biology is unique in this regard, our analysis suggests that global algorithmic field classification approaches that use citation links indiscriminately may struggle to reconstruct research specialties.