Showing papers on "Scientific collaboration network published in 2012"

Dynamic Scientific Co-Authorship Networks

Abstract: Scientific collaboration networks have been studied systematically since 1960 by scholars belonging to various disciplinary backgrounds. As a result, the complex phenomenon of scientific collaboration networks has been investigated within different approaches. Although the term “scientific collaboration network” has different connotations in the literature, we use the term more narrowly to focus on scientific collaboration resulting in co-authored public documents. We broaden this beyond journal articles to include many types of scientific productions in addition to journal articles and books. We insist that these productions are public items available in each field. In this chapter, we focus on the main quantitative approaches dealing with the structure and dynamics of scientific collaboration networks through co-authorized publications. We provide a brief history of social network analysis that serves as a foundation. We further review earlier conceptual classifications of co-authorship networks and distinguish cross-disciplinarily, cross-sectoral and cross-national levels. We couple the newer ideas of “small world” models and “preferential attachment” to older sociological conceptions of scientific collaboration. This is followed by descriptions of deterministic and stochastic models that have been used to study dynamic scientific collaboration networks. We stress the importance of delineating the topology of collaboration networks, understanding micro-level processes and then coupling them. We conclude by outlining the strengths and limitations of various modeling strategies.

The evolution of trade and scientific collaboration networks in the global wine sector: a longitudinal study using network analysis

Abstract: Throughout the past three decades, the global pattern of wine production has undergone fundamental changes, most notably the emergence of New World producers. This article presents a detailed account of the sector's changing global organization from 1974 to 2004 by applying network analysis methods to the evolution of international trade and scientific collaboration networks. We argue that there is a strong mutual interdependence of trade and scientific knowledge production, as a result of which we expect the geographic configuration of global knowledge and trade networks to coevolve. Our results show that, over time, only a few New World wine producers developed trade and scientific collaboration networks that resemble those of traditional Old World producers. They also show that structures of trade and scientific collaboration networks are more alike for Old World than for New World producers, which suggests that, contrary to our expectations, it is particularly Old World producers who may have mainly benefited from participation in international scientific collaboration.

Scientific Output Indicators and Scientific Collaboration Network Mapping in Brazil

Abstract: This is an investigation of Brazilian articles indexed by SCI between 2004 and 2006 with the purpose of understanding the scientific collaboration in the Brazilian community. The 49,046 articles that have been examined show that national scientific output increased every year during the period and that articles were published in a great number of journals, 15.7% of which were national publications. The most productive areas are Chemistry, Biology, Physics and Clinical and Experimental Medicine II. Authors from several institutions were identified, which indicates Brazil has yet to establish a consolidated pattern of publication, since several of the institutions contributed only one article (59.1%). Co-publication between individuals increased in the period, representing about 96% of national output. The mean number of authors per article is 6.3. Scientific output is concentrated in few institutions, mostly public universities located in specific regions. The mean number of institutions per article is 2.4...

Community evolution in a scientific collaboration network

Abstract: A community in a network is a set of nodes with a larger density of intra-community links than inter-community links. Tracking communities in a network via a community life-cycle model can reveal patterns on how the network evolve. Previous models of community life-cycle provided a first step towards analyzing how communities change over time. We introduce an extended life-cycle model having the minimum community size as a parameter. Our model is capable of uncovering anomaly in community evolution and dynamics such as communities with stable or stagnant size. We apply our model to track, and uncover trends in, the evolution of communities of genetic programming researchers. The lifespan of a community measures how long it has lived. The distribution of lifespan in the network of genetic programming researchers is shown to be modeled as an exponential-law, a phenomenon yet to be explored in other empirical networks. We show that our parameter of minimum community size can significantly affect how communities grow over time. The parameter is fine-tuned to detect anomaly in community evolution.

Which topic will you follow

Abstract: Who are the most appropriate candidates to receive a call-for-paper or call-for-participation? What session topics should we propose for a conference of next year? To answer these questions, we need to precisely predict research topics of authors. In this paper, we build a MLR (Multiple Logistic Regression) model to predict the topic-following behavior of an author. By empirical studies, we find that social influence and homophily are two fundamental driving forces of topic diffusion in SCN (Scientific Collaboration Network). Hence, we build the model upon the explanatory variables representing above two driving forces. Extensive experimental results show that our model can consistently achieves good predicting performance. Such results are independent of the tested topics and significantly better than that of state-of-the-art competitor.

Finding homogeneous collections of dense subgraphs using constraint-based data mining approaches

Abstract: The work presented in this thesis deals with data mining approaches for the analysis of attributed graphs. An attributed graph is a graph where properties, encoded by means of attributes, are associated to each vertex. In such data, our objective is the discovery of subgraphs formed by several dense groups of vertices that are homogeneous with respect to the attributes. More precisely, we define the constraint-based extraction of collections of subgraphs densely connected and such that the vertices share enough attributes. To this aim, we propose two new classes of patterns along with sound and complete algorithms to compute them efficiently using constraint-based approaches. The first family of patterns, named Maximal Homogeneous Clique Set (MHCS), contains patterns satisfying constraints on the number of dense subgraphs, on the size of these subgraphs, and on the number of shared attributes. The second class of patterns, named Collection of Homogeneous k-clique Percolated components (CoHoP), is based on a relaxed notion of density in order to handle missing values. Both approaches are used for the analysis of scientific collaboration networks and protein-protein interaction networks. The extracted patterns exhibit structures useful in a decision support process. Indeed, in a scientific collaboration network, the analysis of such structures might give hints to propose new collaborations between researchers working on the same subjects. In a protein-protein interaction network, the analysis of the extracted patterns can be used to study the relationships between modules of proteins involved in similar biological situations. The analysis of the performances, on real and synthetic data, with respect to different attributed graph characteristics, shows that the proposed approaches scale well for large datasets.

Using motif to characterize building block of scientific collaboration networks

Abstract: Global topological analysis of scientific collaboration network has become an important method in discovering the collaboration mechanism among scientists.However,the local structure of these network is still unknow.Focusing on local structure,we generalize the notion of network motifs,defined as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks,and apply algorithms for systematically detecting network motifs in several large-scale scientific collaboration networks.Results show that motifs and anti-motifs of the scientific collaboration networks form a common feature.However,distributions of subgraph concentration of the networks are found different due to cooperation features and levels of development.Furthermore,the network structure can be constructed with a similar bottom-up mechanism.Local structure analysis of scientific collaboration network may help may help define the basic constructing elements and allow an easily interpretable view of the global characteristics of scientific research systems.

Rede de colaboração científica entre universidades brasileiras: uma análise na área de odontologia

Abstract: This paper analyses the scientific collaboration network formed by the Brazilian universities that investigate in dentistry area. The constructed network is based on the published documents in the Scopus (Elsevier) database covering a period of 10 (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities. Cohesion and density of the collaboration network is analyzed, as well as the centrality of the universities as key-actors and the occurrence of subgroups with in the network. Data were analyzed using the software UCINET and NetDraw. The number of documents published by each university was used as an indicator of its scientific production.

Research on the Community Structure and Hub Node of a Scientific Collaboration Network

Abstract: Study on the author collaboration complex network of journals "ISI-SCIE(Science Citation Index Expanded)" in PHARMACOLOGY PHARMACY category,It is found that the collaboration networks have 40 sub-networks,and the node degrees of the maximal connected sub-networks obey the power-law distribution.And this collaboration networks has a smaller characteristic path length and a bigger clustering coefficient.And it also has some key nodes and typical characteristic of Scale-Free and Small-World.Moreover,through the analysis and mine of community structure by the GN algorithm,and evaluating the Hub nodes by degree,betweenness and Pagerank value,it is found that,this collaboration networks have some high cooperation level of scientific research groups and some influential scientists.