On the citation lifecycle of papers with delayed recognition

Delayed recognition is a concept applied to articles that receive very few to no citations for a certain period of time following publication, before becoming actively cited. To determine whether such a time spent in relative obscurity had an effect on subsequent citation patterns, we selected articles that received no citations before the passage of ten full years since publication, investigated the subsequent yearly citations received over a period of 37 years and compared them with the citations received by a group of papers without such a latency period. Our study finds that papers with delayed recognition do not exhibit the typical early peak, then slow decline in citations, but that the vast majority enter decline immediately after their first – and often only – citation. Middling papers’ citations remain stable over their lifetime, whereas the more highly cited papers, some of which fall into the “sleeping beauty” subtype, show non-stop growth in citations received. Finally, papers published in different disciplines exhibit similar behavior and did not differ significantly

A multiple k-means cluster ensemble framework for clustering citation trajectories

Citation maturity time varies for different articles. However, the impact of all articles is measured in a fixed window. Clustering their citation trajectories helps understand the knowledge diffusion process and reveals that not all articles gain immediate success after publication. Moreover, clustering trajectories is necessary for paper impact recommendation algorithms. It is a challenging problem because citation time series exhibit significant variability due to non linear and non stationary characteristics. Prior works propose a set of arbitrary thresholds and a fixed rule based approach. All methods are primarily parameter dependent. Consequently, it leads to inconsistencies while defining similar trajectories and ambiguities regarding their specific number. Most studies only capture extreme trajectories. Thus, a generalised clustering framework is required. This paper proposes a feature based multiple k means cluster ensemble framework. 1,95,783 and 41,732 well cited articles from the Microsoft Academic Graph data are considered for clustering short term (10 year) and long term (30 year) trajectories, respectively. It has linear run time. Four distinct trajectories are obtained Early Rise Rapid Decline (2.2%), Early Rise Slow Decline (45%), Delayed Rise No Decline (53%), and Delayed Rise Slow Decline (0.8%). Individual trajectory differences for two different spans are studied. Most papers exhibit Early Rise Slow Decline and Delayed Rise No Decline patterns. The growth and decay times, cumulative citation distribution, and peak characteristics of individual trajectories are redefined empirically. A detailed comparative study reveals our proposed methodology can detect all distinct trajectory classes.Comment: 29 page

Signals in Science - On the Importance of Signaling in Gaining Attention in Science

Which signals are important in gaining attention in science? For a group of 1,371 scientific articles published in 17 demography journals in the years 1990-1992 we track their influence and discern which signals are important in receiving citations. Three types of signals are examined: the author’s reputation (as producer of the idea), the journal (as the broker of the idea), and the state of uncitedness (as an indication of the assessment by the scientific community of an idea). The empirical analysis points out that, first, the reputation of journals plays an overriding role in gaining attention in science. Second, in contrast to common wisdom, the state of uncitedness does not affect the future probability of being cited. And third, the reputation of a journal may help to get late recognition (so-called ‘sleeping beauties’) as well as generate so-called ‘flash-in-the-pans’: immediately noted articles but apparently not very influential in the long run

Clustering citation histories in the Physical Review

We investigate publications trough their citation histories -- the history events are the citations given to the article by younger publications and the time of the event is the date of publication of the citing article. We propose a methodology, based on spectral clustering, to group citation histories, and the corresponding publications, into communities and apply multinomial logistic regression to provide the revealed communities with semantics in terms of publication features. We study the case of publications from the full Physical Review archive, covering 120 years of physics in all its domains. We discover two clear archetypes of publications -- marathoners and sprinters -- that deviate from the average middle-of-the-roads behaviour, and discuss some publication features, like age of references and type of publication, that are correlated with the membership of a publication into a certain community