Authors:Alireza Abbasi, Liaquat Hossain, Shahadat Uddin, and Kim J. R. Rasmussen
. 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 collaborationnetworks. In this study, based
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.
Authors:Raf Guns, Yu Xian Liu, and Dilruba Mahbuba
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 collaborationnetwork of
Authors:Haiyan Hou, Hildrun Kretschmer, and Zeyuan Liu
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.
research collaboration, forming patterns of co-authorship from many articles is necessary. Using only one article to represent a research collaborationnetwork is too arbitrary and implies bias. Therefore, 55 scholars who published four or more articles in
Award and publication metadata were processed to normalize the names of investigators and authors for the collaborationnetworks. Award amounts were split evenly among investigators and funding programs in determining the normalized amount of funding
Authors:Duk Hee Lee, Il Won Seo, Ho Chull Choe, and Hee Dae Kim
In research activities, the development of collaborationnetworks expands the opportunities to access wide-ranging knowledge sources and catalyses the exchange of ideas to encourage the creation of new knowledge
Authors:Fuyuki Yoshikane, Takayuki Nozawa, Susumu Shibui, and Takafumi Suzuki
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.
Authors:T. S. Evans, R. Lambiotte, and P. Panzarasa
bibliographies, have a number of much larger and relatively complete and detailed collaborationnetworks been documented and analysed (Barabási et al. 2002 , Jones et al. 2008 , Moody 2004 , Newman 2001a , Wuchty et al. 2007 ).
While most of these
Authors:Vincent Larivi?re, Yves Gingras, and Éric Archambault
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, SocialSciences 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.