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. 2006 ; Sonnenwald 2007 ; Suresh et al. 2007 ; Jiang 2008 ; Abbasi and Altman 2010 ; Abbasi et al. 2011 ) for different domains but to our knowledge there is no such study of “steel structures” research collaboration networks. In this study, based

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Abstract  

A study on the network characteristics of two collaboration networks constructed from the ACM and DBLP digital libraries is presented. Different types of generic network models and several examples are reviewed and experimented on re-generating the collaboration networks. The results reveal that while these models can generate the power-law degree distribution sufficiently well, they are not able to capture the other two important dynamic metrics: average distance and clustering coefficient. While all current models result in small average distances, none shows the same tendency as the real networks do. Furthermore all models seem blind to generating large clustering coefficients. To remedy these shortcomings, we propose a new model with promising results. We get closer values for the dynamic measures while having the degree distribution still power-law by having link addition probabilities change over time, and link attachment happen within local neighborhood only or globally, as seen in the two collaboration networks.

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important than central nodes in a small component. Data and methods In order to find out how global Q-measures, local Q-measures and betweenness centrality behave practically, we have applied them to a collaboration network of

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Abstract  

The structure of scientific collaboration networks in scientometrics is investigated at the level of individuals by using bibliographic data of all papers published in the international journal Scientometrics retrieved from the Science Citation Index (SCI) of the years 1978–2004. Combined analysis of social network analysis (SNA), co-occurrence analysis, cluster analysis and frequency analysis of words is explored to reveal: (1) The microstructure of the collaboration network on scientists’ aspects of scientometrics; (2) The major collaborative fields of the whole network and of different collaborative sub-networks; (3) The collaborative center of the collaboration network in scientometrics.

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research collaboration, forming patterns of co-authorship from many articles is necessary. Using only one article to represent a research collaboration network is too arbitrary and implies bias. Therefore, 55 scholars who published four or more articles in

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Award and publication metadata were processed to normalize the names of investigators and authors for the collaboration networks. Award amounts were split evenly among investigators and funding programs in determining the normalized amount of funding

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Introduction In research activities, the development of collaboration networks expands the opportunities to access wide-ranging knowledge sources and catalyses the exchange of ideas to encourage the creation of new knowledge

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Although many studies have analyzed the “synchronic” correlation of properties between authors and their co-authors, the “diachronic” correlation of properties, i.e., the correlation between their subsequent and precedent activity, has not yet been sufficiently studied using quantitative methods. This study pays attention not only to productivity but also the importance in the collaboration network as a measure of the researcher’s activity, and clarifies whether there is any connection between (i) the researcher’s activity subsequent to a collaboration and (ii) the collaborator’s precedent activity, aiming at deriving knowledge about the diachronic effect of collaborators.

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bibliographies, have a number of much larger and relatively complete and detailed collaboration networks been documented and analysed (Barabási et al. 2002 , Jones et al. 2008 , Moody 2004 , Newman 2001a , Wuchty et al. 2007 ). While most of these

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Summary  

A basic dichotomy is generally made between publication practices in the natural sciences and engineering (NSE) on the one hand and social sciences and humanities (SSH) on the other. However, while researchers in the NSE share some common practices with researchers in SSH, the spectrum of practices is broader in the latter. Drawing on data from the CD-ROM versions of the Science Citation Index, Social Sciences Citation Index and the Arts & Humanities Citation Index from 1980 to 2002, this paper compares collaboration patterns in the SSH to those in the NSE. We show that, contrary to a widely held belief, researchers in the social sciences and the humanities do not form a homogeneous category. In fact, collaborative activities of researchers in the social sciences are more comparable to those of researchers in the NSE than in the humanities. Also, we see that language and geographical proximity influences the choice of collaborators in the SSH, but also in the NSE. This empirical analysis, which sheds a new light on the collaborative activities of researchers in the NSE compared to those in the SSH, may have policy implications as granting councils in these fields have a tendency to imitate programs developed for the NSE, without always taking into account the specificity of the humanities.

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