Through discovery of mesoscale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities because some nodes may reasonably be assigned to multiple communities. We have previously reported Iterative K-core Clustering, a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary metamethod for overlapping communities, as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology.

Clustering and community detection in networks are of broad interest and have been the subject of extensive research that spans several fields. We are interested in the relatively narrow question of detecting communities of scientific publications that are linked by citations. These publication communities can be used to identify scientists with shared interests who form communities of researchers. Building on the well-known k -core algorithm, we have developed a modular pipeline to find publication communities with center–periphery structure. Using a quantitative and qualitative approach, we evaluate community finding results on a citation network consisting of over 14 million publications relevant to the field of extracellular vesicles. We compare our approach to communities discovered by the widely used Leiden algorithm for community finding.

Understanding the nature and organization of scientific communities is of broad interest. The “Invisible College” is a historical metaphor for one such type of community that refers to a small group of scientists working on a problem of common interest. The scientific and social behavior of such colleges has been the subject of case studies that have examined limited samples of the scientific enterprise. We introduce a metamethod for large-scale discovery that consists of a pipeline to select themed article clusters, whose authors can then be analyzed. A sample of article clusters produced by this pipeline was reviewed by experts, who inferred significant thematic relatedness within clusters, suggesting that authors linked to such clusters may represent valid communities of practice. We explore properties of the author communities identified by our pipeline, and the publication and citation practices of both typical and highly influential authors. Our study reveals that popular domain-independent criteria for graphical cluster quality must be carefully interpreted in the context of searching for author communities, and also suggests a role for contextual criteria.

Recently, Wu, Wang, and Evans (2019) proposed a new family of indicators, which measure whether a scientific publication is disruptive to a field or tradition of research. Such disruptive influences are characterized by citations to a focal paper, but not its cited references. In this study, we are interested in the question of convergent validity. We used external criteria of newness to examine convergent validity: In the postpublication peer review system of F1000Prime, experts assess papers whether the reported research fulfills these criteria (e.g., reports new findings). This study is based on 120,179 papers from F1000Prime published between 2000 and 2016. In the first part of the study we discuss the indicators. Based on the insights from the discussion, we propose alternate variants of disruption indicators. In the second part, we investigate the convergent validity of the indicators and the (possibly) improved variants. Although the results of a factor analysis show that the different variants measure similar dimensions, the results of regression analyses reveal that one variant ( DI 5 ) performs slightly better than the others.

Cocitation measurements can reveal the extent to which a concept representing a novel combination of existing ideas evolves towards a specialty. The strength of cocitation is represented by its frequency, which accumulates over time. Of interest is whether underlying features associated with the strength of cocitation can be identified. We use the proximal citation network for a given pair of articles ( x , y ) to compute θ , an a priori estimate of the probability of cocitation between x and y, prior to their first cocitation. Thus, low values for θ reflect pairs of articles for which cocitation is presumed less likely. We observe that cocitation frequencies are a composite of power-law and lognormal distributions, and that very high cocitation frequencies are more likely to be composed of pairs with low values of θ , reflecting the impact of a novel combination of ideas. Furthermore, we note that the occurrence of a direct citation between two members of a cocited pair increases with cocitation frequency. Finally, we identify cases of frequently cocited publications that accumulate cocitations after an extended period of dormancy.

Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields. Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference. Our study examines this question using semantically themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.