Exploring publication networks with a local cohesion-maximizing algorithm

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 either by interacting with a fixed network dataset, or by 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 dataset of AMO physics illustrate the properties of MALBA and its potential. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314