Co-author Pairs Research Articles

Quantifying scientific collaboration impact by exploiting collaboration-citation network

Despite the growing interest in exploring the collaboration patterns and the structure of collaboration networks, the impact of collaboration associated with time-varying scholarly networks is hardly known. This paper investigates collaboration and productivity in a science career and quantifies the impact of collaboration in the collaboration-citation network. Moreover, this paper also investigates collaboration patterns and examines the typical duration of research collaborations. A SCIRank model is proposed to quantify the impact of scientific collaboration, which not only reveals the impact of co-author pairs but also identifies scholarly papers with the outstanding impact that leads to Nobel Prize awards.

Coauthor Network Analysis of Journal of Agricultural Education Articles from 2008-2017

The social capital of collaboration is a critical part of the research process. While AAAE supports collaboration and inclusivity, analysis has not occurred for what collaborations are occurring. This study addressed collaboration between researchers via a social network analysis of coauthorship in the Journal of Agricultural Education from 2008 to 2017. There were 587 articles published in that time frame with 593 unique authors. The number of articles published annually and number of authors per article increased during the 10-year period. The majority of authors only published one article in the time frame analyzed. After excluding authors who never collaborated on an article, a social network of 582 coauthors was analyzed. There was a general tendency for the most prolific authors to also be the most connected, though there were some outliers. Of note, the majority of the most connected authors received their terminal degrees from one of three institutions. The majority of the most prolific coauthor pairs were advisor-advisee pairings, and the remainder were individuals who had worked at the same institution during much of the study’s time frame. A prolific coauthor pairing was not necessarily indicative of a connection that was important for bridging authors across the network.

The role of transportation speed in facilitating high skilled teamwork across cities

High skilled workers gain from face to face interactions. If the skilled can move at higher speeds, then knowledge diffusion and idea spillovers are more likely to reach greater distances. This paper measures the knowledge creation consequences associated with the construction of China's high speed rail (HSR) network that connects mega cities, that feature the nation's best universities, to secondary cities. Since bullet trains reduce cross-city commute times, they reduce the cost of face-to-face interactions between skilled workers who work in different cities. Using a database listing research paper publication and citations, we document a complementarity effect between knowledge production and the transportation network. When connected by the HSR, co-author productivity rises, new co-author pairs emerge and more highly productive scientists migrate to the HSR cities.

Who is collaborating with whom? Part I. Mathematical model and methods for empirical testing

Abstract There are two versions in the literature of counting co-author pairs. Whereas the first version leads to a two-dimensional (2-D) power function distribution; the other version shows three-dimensional (3-D) graphs, totally rotatable around and their shapes are visible in space from all possible points of view. As a result, these new 3-D computer graphs, called “Social Gestalts” deliver more comprehensive information about social network structures than simple 2-D power function distributions. The mathematical model of Social Gestalts and the corresponding methods for the 3-D visualization and animation of collaboration networks are presented in Part I of this paper. Fundamental findings in psychology/sociology and physics are used as a basis for the development of this model. The application of these new methods to male and to female networks is shown in Part II. After regression analysis the visualized Social Gestalts are rather identical with the corresponding empirical distributions ( R 2 > 0.99). The structures of female co-authorship networks differ markedly from the structures of the male co-authorship networks. For female co-author pairs’ networks, accentuation of productivity dissimilarities of the pairs is becoming visible but on the contrary, for male co-author pairs’ networks, accentuation of productivity similarities of the pairs is expressed.

Who is collaborating with whom? Part II. Application of the methods to male and to female networks

Abstract The theoretical approach of the mathematical model of Social Gestalts and the corresponding methods for the 3-D visualization and animation of collaboration networks are presented in Part I. The application of these new methods to male and female networks is shown in Part II. After regression analysis the visualized Social Gestalts are rather identically with the corresponding empirical distributions (R2 > 0.99). The structures of female co-authorship networks differ markedly from the structures of the male co-authorship networks. For female co-author pairs’ networks, accentuation of productivity dissimilarities of the pairs is becoming visible but on the contrary, for male co-author pairs’ networks, accentuation of productivity similarities of the pairs is expressed.

Co-authorship pair distribution patterns by gender

This paper examines impact of gender both on publication productivity and on patterns of scientific collaborations in social sciences in Turkey. The research is based on bibliographic data on national level publications in Turkey. It consists of 7,835 papers written by 6,738 scientists. The findings suggest that (1) there are gender differences at publication productivity, participation, presence and contribution; that (2) there are significantly different tendencies at keeping established co-authorship ties for inter-gender and intra-gender pairs; that (3) there are significant regularities exhibited by coauthor pairs based on each partner author's publication productivity and findings further show that (4) regularities are different for inter-gender and intra-gender co-authorships. This study contributes to literature by exemplifying an integrated approach to better examine role of gender in scientific collaborations. In addition to descriptive social network analysis methods, it exploits and adopts parametric models from the literature: (1) Social Gestalt theory, a model based on bi-variate distributions of co-author pairs' frequencies; (2) Lotka's power law distribution on publication productivity of single authors; (3) Power law distributions of co-author pairs' frequencies.

Who Is Collaborating with Whom in Science? Explanation of a Fundamental Principle

Since many decades power functions are well-known in counting single scientists or co-author pairs in social networks. However, in this paper a developed procedure for visualizing a bivariate distribution of co-author pairs’ frequencies hence producing three-dimensional graphs is presented. This distribution is explained by a fundamental principle of social group formation and described by a mathematical model. This model is applied to 52 co-authorship networks. For 96% of them the squared multiple R is larger than 0.98 and for 77% of the 52 networks even larger than 0.99. The visualized social Gestalts in form of three-dimensional graphs are rather identically with the corresponding empirical distributions. Question: Can we expect a general validity of this mathematical model for co-authorship networks?

Co-author Pairs’ frequencies of the National Physical Laboratory India

Special collaboration structures (Gestalts) are studied on the basis of the publications data on papers published by the National Physical Laboratory, India, during 1997–2008. The corresponding three-dimensional distribution of the co-author pairs’ frequencies is compared with the distribution of the National Physical Laboratory, UK, as well as with distributions of two journals. The regularities for these four three-dimensional distributions are described by the same mathematical model (Social Gestalts). The varying shapes of these distributions are probably caused by the different research conditions. These new empirical results should initiate future research about the corresponding theoretical background followed by further empirical studies.

Distribution of Co-Author Pairs’Frequencies in Energy Science and Technology

Compared with Lotka’s law, H. Kretschmer has referred to that the distribution of co-author pairs’ frequencies could be ideally reflected by a social gestalt. Among the former studies, small data sets from one journal and large data from several journals were investigated to verify these assumptions. In the present study, we will examine the data sets from a certain scientific subject “Energy Science and Technology” and its subfield “Solar Energy”, in order to testify that different configurations of social gestalt could reflect the characteristics of the cooperation in social networks, and meanwhile the data size as one of the factor will be proved, which could cause the convex or concave configurations of social gestalt.

The shift towards multi‐disciplinarity in information science

AbstractThis article analyzes the collaboration trends, authorship and keywords of all research articles published in the Journal of American Society for Information Science and Technology (JASIST). Comparing the articles between two 10‐year periods, namely, 1988–1997 and 1998–2007, the three‐fold objectives are to analyze the shifts in (a) authors' collaboration trends (b) top authors, their affiliations as well as the pattern of coauthorship among them, and (c) top keywords and the subdisciplines from which they emerge. The findings reveal a distinct tendency towards collaboration among authors, with external collaborations becoming more prevalent. Top authors have grown in diversity from those being affiliated predominantly with library/information‐related departments to include those from information systems management, information technology, businesss, and the humanities. Amid heterogeneous clusters of collaboration among top authors, strongly connected cross‐disciplinary coauthor pairs have become more prevalent. Correspondingly, the distribution of top keywords' occurrences that leans heavily on core information science has shifted towards other subdisciplines such as information technology and sociobehavioral science.

A model for the size‐frequency function of coauthor pairs

AbstractLotka's law was formulated to describe the number of authors with a certain number of publications. Empirical results (Morris & Goldstein, 2007) indicate that Lotka's law is also valid if one counts the number of publications of coauthor pairs. This article gives a simple model proving this to be true, with the same Lotka exponent, if the number of coauthored papers is proportional to the number of papers of the individual coauthors. Under the assumption that this number of coauthored papers is more than proportional to the number of papers of the individual authors (to be explained in the article), we can prove that the size‐frequency function of coauthor pairs is Lotkaian with an exponent that is higher than that of the Lotka function of individual authors, a fact that is confirmed in experimental results.

Studies in Co-authorship Pairs Distribution: Part-1

There are different versions of counting co-author pairs. We refer to two of them in the present paper including two corresponding hypotheses. Whereas the first version leads to a power function distribution of the co-author pairs the other version shows a bivariate distribution of co-author pairs’ frequencies hence producing threedimensional graphs. The regularities for the second kind of distributions are very different from simple power law distributions. These new regularities may be described by a model for the intensity function of interpersonal attraction (Social Gestalt). Complementarities are a crucial determinant of this model. In conjunction with these complementarities, various shapes of the distribution of observed co-author pairs’ frequencies emerge. As the difference between the two shapes increases, the correlation between these two shapes decreases. After mixing these two shapes a new shape of social Gestalt emerges which can be successfully described with the help of the mathematical function for a social Gestalt. The change of any shape to any other shape of the social Gestalt can be shown by simulation (or morphing). For comparison of two or more shapes the overlay of these shapes into a single frame is possible: either overlay of standardized shapes or of the originals. The present paper is focused on a theoretical approach verified by empirical studies explained in two papers published in this issue (Guo et.al.[1] and Kundra et.al[2]).

Studies in Co-authorship Pairs Distribution: Part-2

Previous studies have presented well-ordered collaboration structures of co-author pairs. Kretschmer and Kretschmer have shown that, for high impact SCI journals, the distributions of co-author pairs’ frequencies can be considered to be a reflection of a social Gestalt. This study is an extension of that work to the social sciences, namely eleven journals in women’s and gender studies. After overlapping or mixing the eleven distributions of co-author pairs, the data reveal the same tendency to a social Gestalt. However the shapes of social Gestalts for gender studies differs markedly from the corresponding shapes of the high impact SCI Journals. Simulation shows that the change of the shape from the Gestalt of the mixed journals of gender studies towards the shape of the four mixed SCI journals could be continuous.

Distribution of Co-Author Pairs’ Frequencies of the Journal of Information Technology

Lotka’s Law originally calculated only the frequencies of single or first authors. However, given the growth and popularity of scientific collaboration, the distribution of co-author pairs’ frequencies should also be considered. Based on the previous researchers’ results, this article will demonstrate that the distribution of co-author pairs’ frequencies can be reflected by a social gestalt and that there are different shapes of this social gestalt. Results for the distribution of co-author pairs in journals are shown for the co-authorship data of the Journal of Information Technology (1994–2007).

Distribution of Co-Author Pairs’ Frequencies of the Journal of Biological Chemistry Explained as Social Gestalt

In single authored bibliographies only single scientist distribution can be found. Wheras in multi-authored bibliographies single scientists distribution, pairs distribution, triples distribution, etc., can be presented. Whith regard to Lotka’s law single scientists P distribution (both in single authored and in multi-authored bibliographies) is of interest. In the future pairs P,Q distribution, triples P,Q,R distribution, etc. should be considered. Starting with pair distribution, the question arises: Is there any regularity or well-ordered structure for the distribution of co-author pairs in the journals on the analogy to Lotka’s law for the distribution of single authors? Usually, “laws” or “regularities” in information science (for example Lotka’s law) have their mathematical descriptions of observed the data in form of functions. The explanations of these phenomena are, however, mostly missing. By contrast, the derivation of a formula for describing the distribution of the number of co-author pairs is presented on the basis of well-known regularities in socio-psychology or sociology in conjunction with Gestalt theory, as an explanation for well-ordered collaboration structures and production of scientific literature. The assumed regularities for the distribution of co-author pairs in journals could be shown in the co-authorship data (1980–1998) of the Journal of Biological Chemistry.

Lotka's distribution and distribution of co-author pairs’ frequencies

The original Lotka's Law refers to single scientist distribution, i.e. the frequency of authors A i with i publications per author is a function of i: A i = f( i). However, with increasing collaboration in science and in technology the study of the frequency of pairs or triples of co-authors is highly relevant. Starting with pair distribution well-ordered collaboration structures of co-author pairs will be presented, i.e. the frequency of co-author pairs N ij between authors with i publications per author and authors with j publications per author is a function of i and j: N ij = f( i, j) using the normal count procedure for counting i or j. We have assumed that the distribution of co-author pairs’ frequencies can be considered to be reflection of a social Gestalt and therefore can be described by the corresponding mathematical function based on well-known general characteristics of structures in interpersonal relations in social networks. We have shown that this model of social Gestalts can better explain the distribution of co-author pairs than by a simple bivariate function in analogy to Lotka's Law. This model is based on both the Gestalt theory and the old Chinese Yin/Yang theory.

A bibliometric analysis of collaboration in a medical specialty

Investigating the relationships found in the documentation of a subject field is one method of examining the communication taking place in that field. Bibliometrics provides an objective method for this type of investigation. Coauthorship, while intuitively seeming to indicate strong communication links, nevertheless has been shown to produce graphical structures that vary with changes in threshold. Having determined that clustering structure does exist in the data, preferred partitions are identified as those least likely to have occurred by chance. Further analysis is made to test that the preferred or “meaningful” structures produced from the coauthor relationship do indeed correspond with empirical evidence of “meaning”. A small dataset of 371 authors and 550 coauthor pairs is used to investigate correspondence between experimental structures and empirical evidence. Results show that components of the experimental structures are largely consistent with subject content groups as determined by index terms. Geographic focus accounts for about half the cases showing term overlap. Hence, we have some evidence that bibliometric structures determined from the coauthor relationship may be consistent with networks of communication. If this continues to be documented by further research, bibliometric analysis of coauthor relationships found in the scholarly communication of a subject area can become a basic tool for communication research.

An investigation of the coauthor graph

The structure of coauthor graphs and the statistical validity of the associated author partitions are investigated as a function of productivity and collaborative thresholds. The productivity threshold determines the number of authors (points) in a coauthor graph, and the collaborative threshold determines the number of coauthor pairs (lines) in the graph. The statistical validity of author partitions is determined by the random‐graph hypothesis. The results show that for “small” databases, statistically preferred partitions occur when all authors and coauthor pairs appear in the graph. For “large” databases, statistically preferred partitions occur when authors and coauthor pairs who publish only one article are excluded from the graph. Unlike other bibliometric relationships, the highly selective nature of the collaborative relationship produces a wide range of threshold values for which the associated partitions are statistically valid. It remains to be shown how the statistical validity of partitions is related to the empirical significance of the same partitions. © 1987 John Wiley & Sons, Inc.