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This brief communication finds a clear and universal inequality of authors' reference reuse behaviour. We observe that a few references are reused many times in an author's oeuvre while most of his or her references only occur in the reference list for quite a limited number of times. A power law distribution depicts such an inequality. We particularly utilise the power value, α , to characterise the nuanced difference of such inequalities. A pilot study based upon Microsoft Academic Graph (MAG) shows that the α tends to be normally distributed, regardless of whether it is from a citing or a cited perspective. Our empirical study also reveals that the α of highly cited publications tends to be greater than that of lowly cited ones, yet we also observe a saturation when the number of citations increases. [ABSTRACT FROM AUTHOR]

Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications. [ABSTRACT FROM AUTHOR]

Correlation has become a fundamental method for information science. However, correlations are limited in making concrete decisions. In this article, we detail how causal inference could be utilised in the field of information science. There are six main steps of implementing matching for causal inference, namely, selecting candidate control variables, determining control variables, calculating similarities among all samples, forming control group, examining the performance of control group and estimating causal effects. As an example, this article applies causal inference to investigate whether Nobel Physics award increases the after-award citations. The method is presented in a step-by-step manner so that researchers can reproduce our analysis in the future. [ABSTRACT FROM AUTHOR]

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important. [ABSTRACT FROM AUTHOR]

Scientometrics has many citation‐based measurements for characterizing diversity, but most of these measurements depend on human‐designed categories and the granularity of discipline classifications sometimes does not allow in‐depth analysis. As such, the current paper proposes a new measurement for quantifying journals' diversity by utilizing the abstracts of scientific publications in journals, namely topic diversity (TD). Specifically, we apply a topic detection method to extract fine‐grained topics, rather than disciplines, in journals and adapt certain diversity indicators to calculate TD. Since TD only needs as inputs abstracts of publications rather than citing relationships between publications, this measurement has the potential to be widely used in scientometrics. [ABSTRACT FROM AUTHOR]

Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high‐order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high‐order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties—such as depth, width, structural virality, and size—which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans‐domain (or indirectly related domain) works than to receive only intradomain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history. [ABSTRACT FROM AUTHOR]

Traditionally, publication citation networks are regarded as acyclic, that is, no loops in the network as an earlier published article cannot cite a later published article. However, due to the accessibility of pre-print versions of articles, there might be some loops in a publication citation network. This article presents a descriptive statistic on loops in publication citation networks of computer science and physics by employing a network-based indicator, namely, strongly connected component (SCC). By employing computer science and physics disciplines publications from the Web of Science database as examples, this article examines the count of loops, how the count changes over time and how the count relates to the published year difference between publications within the loop in the citation network. Some common structural patterns are also extracted and analysed; we observe that the two disciplines share the most frequent patterns though there exist some minor differences. Moreover, we find that self-citations in terms of authors, authors' institutions and journals contribute to the formation of loops in publication citation networks. [ABSTRACT FROM AUTHOR]

This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high‐, medium‐, and low‐cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium‐ and low‐cited publications. Moreover, as N increases, the difference in receiving new citations among high‐, medium‐, and low‐cited publications increases quite significantly. [ABSTRACT FROM AUTHOR]

Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the “low p and low q” pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation. [ABSTRACT FROM AUTHOR]

Author cocitation analysis (ACA) is a branch of bibliometrics and knowledge representation that aims to map knowledge domains. However, ACA has been criticized because count-based measurement is too simple, and resulting maps are insufficiently informative. Since different scholarly relationships, e.g., coauthorship and author bibliographic coupling relationships, can extract out different relationships among authors in various perspectives, combining them with ACA for constructing knowledge domain mappings is our major purpose. The proposed method constructs the hybrid matrix from all relationships in four steps: relationship normalization, calculating the similarity between scholarly relationships, calculating adjustment parameters, and constructing hybrid relationships. The important parameters for integrating these matrices are calculated according to the distance in the hyperspace transformed from the similarity among the scholarly relationships by exploratory factor analysis. Compared with ACA, the results of the proposed method show: (1) More sub-fields in the given discipline can be identified when combining other scholarly relationships; (2) The more scholarly relationships added into ACA, the more details in terms of research area the method will find; (3) Good visualization in clustering is depicted when we combine other scholarly relationships. As a result, the proposed method offers a good choice to understand researchers and to map knowledge domains in a study field for integrating more scholarly relationships at the same time. [ABSTRACT FROM AUTHOR]