Searching for temporal patterns in the time series of publications of authors in a research specialty (original) (raw)
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Research Collaboration Influence Analysis Using Dynamic Co-authorship and Citation Networks
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Collaborative research is increasing in terms of publications, skills, and formal interactions, which certainly makes it the hotspot in both academia and the industrial sector. Knowing the factors and behavior of dynamic collaboration network provides insights that helps in improving the researcher's profile and coordinator's productivity of research. Despite rapid developments in the research collaboration process with various outcomes, its validity is still difficult to address. Existing approaches have used bibliometric network analysis with different aspects to understand collaboration patterns that measure the quality of their corresponding relationships. At this point in time, we would like to investigate an efficient method to outline the credibility of findings in publication-author relations. In this research, we propose a new collaboration method to analyze the structure of research articles using four types of graphs for discerning authors' influence. We apply different combinations of network relationships and bibliometric analysis on the G-index parameter to disclose their interrelated differences. Our model is designed to find the dynamic indicators of co-authored collaboration with an influence on the author's behavior in terms of change in research area/interest. In the research we investigate the dynamic relations in an academic field using metadata of openly available articles and collaborating international authors in interrelated areas/domains. Based on filtered evidence of relationship networks and their statistical results, the research shows an increment in productivity and better influence over time.
Program, 2014
Purpose – The purpose of this paper is to compute and analyze the topological properties of co-authorship network formed between earth scientists in India. As a case study, the authors evaluate bibliographic data of authors who have contributed research articles in the Journal of the Geological Society of India, a premier earth science journal in India. Design/methodology/approach – Research articles totaling 3,903 records from 1970 to 2011 were harvested from the ISI Web of Science SCI database and analyzed using Social Network Analysis. Findings – The author productivity in terms of number of papers published followed Lotka's law with β=2.1027. A dense giant component was detected that spanned 73 percent of the network with a density of 0.0017 and clustering coefficient of 0.631, suggesting high level of knowledge diffusion and a rapid flow of information and creativity in this network. Local metrics were calculated using degree, betweenness and closeness centralities. A stron...
Motifs in co-authorship networks and their relation to the impact of scientific publications
The European Physical Journal B, 2011
Co-authorship networks, where the nodes are authors and a link indicates joint publications, are very helpful representations for studying the processes that shape the scientific community. At the same time, they are social networks with a large amount of data available and can thus serve as vehicles for analyzing social phenomena in general. Previous work on co-authorship networks concentrates on statistical properties on the scale of individual authors and individual publications within the network (e.g., citation distribution, degree distribution), on properties of the network as a whole (e.g., modularity, connectedness), or on the topological function of single authors (e.g., distance, betweenness). Here we show that the success of individual authors or publications depends unexpectedly strongly on an intermediate scale in co-authorship networks. For two large-scale data sets, CiteSeerX and DBLP, we analyze the correlation of (three-and four-node) network motifs with citation frequencies. We find that the average citation frequency of a group of authors depends on the motifs these authors form. In particular, a box motif (four authors forming a closed chain) has the highest average citation frequency per link. This result is robust across the two databases, across different ways of mapping the citation frequencies of publications onto the (uni-partite) co-authorship graph, and over time. We also relate this topological observation to the underlying social and socio-scientific processes that have been shaping the networks. We argue that the box motif may be an interesting category in a broad range of social and technical networks.
Structuring scientific activities by co-author analysis
Scientometrics, 1991
In this paper we apply 'co-author analysis' to create from a large set of publications dusters of collaborating researchers within a faculty of chemical engineering. Results have been discussed with an expert. The co-author clusters appeared to be meaningful, with respect to the identification of research groups, the relations within these groups, as well as to relations between these groups and changes in time. Also differences between ISI-bascd and non-ISI based maps proved to be consistent with the expert's opinion. Many clusters teprc.sent collaborating authors grouped around a full professor, mostly the department chairman. Coauthor analysis can be used, for example, as an important tool in evaluative bibliometrics in order to make a first identification of research groups in 'unknown' universities or organizations. indicators of research collaboration in a discipline (Lawani, 1986; Ajiferuke et al., 1988). Price and Beaver (1966) were one of the first who used co-author relationships to investigate social structures and influence in science, and, more specifically, communication networks. In particularly, they investigated the collaboration in 'invisible colleges' by sorting manually a group of authors, placing them together with those who had collaborated with one specific author, and also with those who had collaborated with the collaborators of this specific author, and so on. They found that 23 of a total of 122 groups consisted of just one single individual, and one large group :~ Dedicated to the memory of Michael J. Moravcsik
Trend and Efficiency Analysis of Co-authorship Network
Although co-authorship in scientific research has a long history the analysis of co-authorship network to explore scientific collaboration among authors is a relatively new research area. Studies of current literature about co-authorship networks mostly give emphasis to understand patterns of scientific collaborations, to capture collaborative statistics, and to propose valid and reliable measures for identifying prominent author(s). However, there is no such study in the literature which conducts a longitudinal analysis of co-authorship networks. Using a dataset that spans over twenty years, this paper attempts to explore efficiency and trend of co-authorship networks. Two scientists are considered connected if they have co-authored a paper, and these types of connections between two scientists eventually constitute co-authorship networks. Co-authorship networks evolve among researchers over time in specific research domains as well as in interdisciplinary research areas. Scientists from diverse research areas and different geographical locations may participate in one specific co-authorship network whereas an individual scientist may belong to different co-authorship networks. In this paper, we study a longitudinal co-authorship network of a specific scientific research area. By applying approaches to analyze longitudinal network data, in addition to known methods and measures of current co-authorship literature, we explore a co-authorship network of a relatively young and emerging research discipline to understand its trend of evolution pattern and proximity of efficiency. Keywords: co-authorship network; trend analysis; efficiency analysis; inter-country collaboration
Scientometrics, 2010
This paper focuses on methods to study patterns of collaboration in co-authorship networks at the mesoscopic level. We combine qualitative methods (participant interviews) with quantitative methods (network analysis) and demonstrate the application and value of our approach in a case study comparing three research fields in chemistry. A mesoscopic level of analysis means that in addition to the basic analytic unit of the individual researcher as node in a co-author network, we base our analysis on the observed modular structure of co-author networks. We interpret the clustering of authors into groups as bibliometric footprints of the basic collective units of knowledge production in a research specialty. We find two types of coauthor-linking patterns between author clusters that we interpret as representing two different forms of cooperative behavior, transfer-type connections due to career migrations or one-off services rendered, and stronger, dedicated inter-group collaboration. Hence the generic coauthor network of a research specialty can be understood as the overlay of two distinct types of cooperative networks between groups of authors publishing in a research specialty. We show how our analytic approach exposes field specific differences in the social organization of research.
Topography and Dynamics of Co-author Networks
Co-author networks have become the center of the attention of both scientometrics and network researches during the last decade. In this article I put more emphasis on the scientometrics side, I compare the actual and the international results related to my topic.
Detecting research groups in coauthorship networks
2008
From the perspective of Library Science and Information Science, little research has yet been conducted on scientific networking and its possible uses in ascertaining the composition of research groups, the differences in associations between specialities or departments, and the different policies that may be followed in this regard, depending on the institution or the domain analyzed. Traditionally, most studies on scientific collaboration have been geared to analyzing output, be it international or domestic, of a given scientific discipline or a research institution. Studies on smaller units such as departments or research groups are less common.
Network analysis of temporal trends in scholarly research productivity
Journal of Informetrics, 2012
We propose a method to identify the journals or proceedings that are most highly esteemed by a research group over some time frame. Using open publication databases, we identify the experts in the community, and analyse their publication pattern, and then use this as a guideline for evaluating scientific outputs of other groups of researchers publishing in the same domain. To illustrate the practicality of our method, we analyse the scientific output of Korean researchers in the security subject domain from 2004 to 2009, and comparing this groups' output with that of well-known researchers. Our empirical analysis demonstrates that there is a persistent gap between these two research groups' publications impact over this period, although the absolute number of journal publications greatly increased over recent years.
Chapter 6 Dynamic scientific co-authorship networks
2013
Network studies of science greatly advance our understanding of both the knowledge-creation process and the flow of knowledge in society. As noted in the introductory chapter, science can be defined fruitfully as a social network of scientists together with the cognitive network of knowledge items (Boerner et al, 2010). The cognitive structure of science consists of relationships between scientific ideas, and the social structure of science is mostly manifested as relationships between scientists. Here, we confine our attention to these relations. In particular, co-authorship networks among scientists are a particularly important part of the collaborative social structure of science. Modern science increasingly involves “collaborative research", and this is integral to the social structure of science. Ziman argues that the organizational units of modern science are groups and not individuals (Ziman, 1994, pp. 227).1 Namely, co-authorship in science presents a more substantial i...