Showing papers on "Scientific collaboration network published in 2009"

Differences between normal and shuffled texts: structural properties of weighted networks

Abstract: In this paper we deal with the structural properties of weighted networks. Starting from an empirical analysis of a linguistic network, we analyze the differences between the statistical properties of a real and a shuffled network. We show that the scale-free degree distribution and the scale-free weight distribution are induced by the scale-free strength distribution, that is Zipf's law. We test the result on a scientific collaboration network, that is a social network, and we define a measure – the vertex selectivity – that can distinguish a real network from a shuffled network. We prove, via an ad hoc stochastic growing network with second order correlations, that this measure can effectively capture the correlations within the topology of the network.

Effect of Inventor Status on Intra-Organizational Innovation Evolution

Abstract: Innovation is one of the primary characteristics that separates successful from unsuccessful organizations. Organizations have a choice in selecting knowledge that is recombined to produce new innovations. The selection of knowledge is influenced by the status of inventors in an organization’s internal knowledge network. In this study, we model knowledge flow within an organization and contend that it exhibits unique characteristics not incorporated in most social network measures. Using the model, we also propose a new measure based on random walks and team identification and use it to examine innovation selection in a large organization. Using empirical methods, we find that inventor status determined by the new measure had a significant positive relationship with the likelihood that his/her knowledge would be selected for recombination. We believe that the new measure in addition to modeling knowledge flow in a scientific collaboration network helps better understand how innovation evolves within organizations.

Filtering of complex systems using overlapping tree networks

Abstract: We introduce a technique that is capable to filter out information from complex systems, by mapping them to networks, and extracting a subgraph with the strongest links. This idea is based on the Minimum Spanning Tree, and it can be applied to sets of graphs that have as links different sets of interactions among the system's elements, which are described as network nodes. It can also be applied to correlation-based graphs, where the links are weighted and represent the correlation strength between all pairs of nodes. We applied this method to the European scientific collaboration network, which is composed of all the projects supported by the European Framework Program FP6, and also to the correlation-based network of the 100 highest capitalized stocks traded in the New York Stock Exchange. For both cases we identified meaningful structures, such as a strongly interconnected community of countries that play an important role in the collaboration network, and clusters of stocks belonging to different sectors of economic activity, which gives significant information about the investigated systems.

Scientific collaboration network in “metrical studies”: a co-authorship study using the scielo information science periodicals

Abstract: This study is aimed at analyzing the scientific collaboration network in the Information Science area, regarding the theme “metrical studies”, based on institutional coauthorships in periodicals published on line by Scientific Electronic Library Online (SciELO), electronic library that comprises a selected collection of Brazilian scientific periodicals, in the following publications: Ciencia da Informacao and Perspectivas em Ciencia da Informacao. The adopted research procedure was the survey of published numbers, including a total of 53 papers related to the topic under study. Initially, 388 papers were worked on, and 53 of them (13,7%, between the two periodicals) are related to this topic. Software Pajek was used in order to construct the scientific collaboration network based on co-authorship, and Statistical Package for the Social Sciences (SPSS) was used for cluster analysis, by using Ward’s method and the distance measures were squared Euclidean with the standardized variables. The data were presented as an aggregation of communities, sometimes isolated, other times forming a configuration of a scientific collaboration network already established, but not dense.

The collaboration network in China's management science

Abstract: By using methods of bibliometrics and social network analysis, we find that the scientific collaboration network in China's management science is a scale-free small world network. Based on statistical analysis of the key network properties (L, the average shortest path length; C, the clustering coefficient; the node degree distribution), we explored the relationships between L and the size of China's management science collaboration network, between C and the network density, and between the degree distribution and the strength of node connectivity. The combination of these results shows the current status of China's management science as an academic field. We also divided the period of study into 3 sub-periods and performed analysis on each sub-period. The temporal study shows the trend in China's management science research. By looking into the authors with high node degrees, we also find the hot research area, the most active researchers and organizations.

An in-depth longitudinal analysis of mixing patterns in a small scientific collaboration network

Abstract: Many investigations of scientific collaboration are based on large-scale statistical analyses of networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a small-scale network of scientific collaboration (N = 291) constructed from the bibliographic record of a research center involved in the development and application of sensor network technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortativity mixing of these node characteristics: academic department, affiliation, position, and country of origin of the individuals in the network. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.

Empirical Analysis of a China Scientists Collaboration Network

Abstract: This paper studies the scientific collaboration network(SCN) based on the papers published on Chinese Science Bulletin during the past 20 years(1988-2007)According to the statistics and complexity theory,we find that,the productivity of scientists follows a power-law distribution and the distribution of the size of collaboration decays in an exponential wayThere is a distinct maximal connected subgraph in the SCN and the subgraph possesses notable clustering and small-world properties,and community and hierarchical structuresThe degree distribution of the SCN is between exponential and power law,and approximately follows lognormal distribution;the distribution of the size of community also shows long tail propertyMoreover,we apply three indexes to study the impact of hub nodes

An Evaluation Method for the Performance of Research Personnel Based on Scientific Collaboration Networks

Abstract: The evaluation of the performance of research personnel is full of challenges. The paper treats this problem as the node importance measurement within the scientific collaboration network (SCN), which is formed through the author information from academic papers in Chinese. In the study, the author's status is measured by the node degree in the SCN, and the strength of collaborative relations between authors is measured by edges' weights. If both the value of the node degree and the weights of edges are large enough, then the current node is tend to be important, in other words, the author represented by this node is very important in terms of his/her status and collaborative relations in the SCN. Case study shows that the proposed method can identify the important nodes in the SCN, i.e. key research personnel in the organization, and evaluate the performance in terms of their academic papers published in the high quality journals. This finding is beneficial for crisis management of key talent loss in organizations.