A new approach to analyzing patterns of collaboration in co-authorship networks: mesoscopic analysis and interpretation (original) (raw)
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ISSI 2009, 2009
We are investigating the manner in which different scientific fields differ in their communication cultures. Our research methodology combines quantitative (graph-theoretic) and qualitative (ethnographic) techniques, which mutually inform each other. In this paper, we present the results of a case study on the identification and classification of scientific collaborations based on structural patterns in co-authorship networks. We investigate three co-authorship networks in specialised subfields of organic chemistry and physical chemistry using an information-theoretic clustering algorithm by Rosvall et al. to extract the modular structure of the networks and thereby investigate the mesoscopic structures of these networks. The clusters found mostly correspond to hierarchically organised research groups or multi-centre research networks. Based on the co-author links between these clusters we analyse group and network interactions, using interviews with participants from the fields in question to inform our interpretation. Structurally one can distinguish two broad classes of linking patterns between clusters: 'exclusive' connections where a single node (author) connects two clusters, and 'manyto-many' connections where substantial fractions of the nodes of both clusters are involved. Within these broad classes one can find further subclasses that correspond to typical collaboration and mobility events.
Toward a mesoscopic analysis of the temporal evolution of scientific collaboration networks
This poster reports on our latest results in a multiyear project that employs a mixed network analytic and ethnographic approach to understand the factors underlying field-specific attitudes towards openness and sharing of scholarly data. We report initial results of adding a temporal dimension to an analysis of scientific collaboration networks that provide evidence for comparative study of community structures and collaboration patterns across scientific fields. The addition of a temporal dimension to the analysis allows us to study the dynamic processes involved in the evolution of a scientific community and to determine field specific patterns. Further, it improves the accuracy with which the internal structures of scientific collectives can be resolved. This ongoing work advances an ethnographically grounded approach to the mesoscopic analysis of collaboration networks. Supported by ethnographic insights, we can connect mesoscopic network features to notions of research groups, group leadership and implied seniority, intergroup collaboration, between group migration, and ephemeral one-off exchanges. Eventually, a mesoscopic perspective should allow us to significantly improve the validity of models to explain network evolution.
Issues in the analysis of co-authorship networks
Scientific collaboration is a complex phenomenon that improves the sharing of competences and the production of new scientific knowledge. Social Network Analysis is often used to describe the scientific collaboration patterns defined by co-authorship relations. Different phases of the analysis of collaboration are related to: data collection, network boundary setting, relational data matrix definition, data analysis and interpretation of results. The aim of this paper is to point out some issues that arise in these different phases, highlighting: i) the use of local archive versus international bibliographic databases; ii) the use of different approaches for setting boundary in a whole-network; iii) the definition of coauthorship data matrix (binary and weighted ties) and iv) the analysis and the interpretation of network measures for co-authorship data. We discuss the different choices that can be made in these phases, within an illustrative example on real data which refer to scientific collaboration among researchers affiliated with an academic institution. In particular, we compare global and actor-level network measures computed from binary and weighted coauthorship networks in different scientific fields. Keywords Bibliographic data, Scientific Collaboration, Social Network Analysis, Weighted Networks
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...
Co-authorship networks: Collaborative research structures at the journal level
Present-day research is, in most cases, the outcome of collaborative research, as evidenced by the fact that most papers are authored by two or more researchers. This study's general goal was to examine the evolution and structure of scientific collaborative networks revealed by papers published in the Tourism & Management Studies journal over a five-year period, from 2011 to 2015, as well as to represent these networks graphically. In this paper, we seek to offer a clear assessment of intra-institutional, inter-institutional and international collaborations and to identify primary author networks and the role of gender in their composition. To reach these goals, we used a combination of bibliometric analysis with social network analysis. The results demonstrate that geographic proximity and linguistic affinity play a substantial role in scientific collaboration between institutions. In fact, most papers result from collaborative research involving two or more authors from the same institution. A gender analysis of the universe of authors and co-authors and of the role of women in the composition of co-authorship networks demonstrated that most networks include women and that, in most networks, women have a leading position, which is consistent with their weight (51.3%) in the universe of authors. This is one of the first studies to demonstrate that women are taking the lead in tourism and management research.
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
Scientometrics, 2012
The paper introduces the use of blockmodeling in the micro-level study of the internal structure of co-authorship networks over time. Variations in scientific productivity and researcher or research group visibility were determined by observing authors' role in the core-periphery structure and crossing this information with bibliometric data. Three techniques were applied to represent the structure of collaborative science: (1) the blockmodeling; (2) the Kamada-Kawai algorithm based on the similarities in co-authorships present in the documents analysed; (3) bibliometrics to determine output volume, impact and degree of collaboration from the bibliographic data drawn from publications. The goal was to determine the extent to which the use of these two complementary approaches, in conjunction with bibliometric data, provides greater insight into the structure and characteristics of a given field of scientific endeavour. The paper describes certain features of Pajek software and how it can be used to study research group composition, structure and dynamics. The approach combines bibliometric and social network analysis to explore scientific collaboration networks and monitor individual and group careers from new perspectives. Its application on a small-scale case study is intended as an example and can be used in other disciplines. It may be very useful for the appraisal of scientific developments. © 2012 Akademiai Kiado, Budapest, Hungary.
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...
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.
Scientific Co‐Authorship Networks
Advances in Network Clustering and Blockmodeling, 2019
Network studies of science offer researchers a great insight into the dynamics of knowledge creation and the social structure of scientific society. The flow of ideas and overall cognitive structure of the scientific community is observed through citations between scientific contributions, usually manifested as patents or papers published in scientific journals. The social structure of this society consists of relationships among scientists. De Haan [10] suggests six operationalized indicators of collaborative relations between scientists: coauthorship; shared editorship of publications; shared supervision of PhD projects; writing a research proposal together; participation in formal research programs; and shared organization of scientific conferences. Due to accessibility and the ease of acquiring data through bibliographic databases, most scientific collaboration analyses are performed on co-authorship data, which play a particularly important role in research into the collaborative social structure of science. Coauthorship networks are personal networks in which the vertices represent authors, and two authors are connected by a tie if they co-authored one or more publications. These ties are necessarily symmetric. The study of community structures through scientific co-authorship is especially important because scientific (sub)disciplines can often display local properties that differ greatly from the properties of the scientific network as a whole. Co-authorship