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András Schubert

Bio: András Schubert is an academic researcher from Hungarian Academy of Sciences. The author has contributed to research in topics: Scientometrics & Citation impact. The author has an hindex of 49, co-authored 204 publications receiving 12734 citations. Previous affiliations of András Schubert include Semmelweis University & University of Agricultural Sciences, Dharwad.


Papers
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01 Mar 2002
TL;DR: The results indicate that the co-authorship network of scientists is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links, and a simple model is proposed that captures the network's time evolution.
Abstract: The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it o8ers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991–98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, a8ecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network’s time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks. c

2,277 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the evolution of the co-authorship network of scientists and found that the network is scale-free and the network evolution is governed by preferential attachment, a8ecting both internal and external links.
Abstract: The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it o8ers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991–98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, a8ecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network’s time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks. c

2,193 citations

Book ChapterDOI
01 Jan 2004
TL;DR: In this article, scientific collaboration is considered both at individual and national levels, with special focus given to multinational collaborations, and the changes, to great extent, can be attributed to the universal tendencies of globalisation and the political restructuring of Europe.
Abstract: Co-authorship is one of the most tangible and well documented forms of scientific collaboration Almost every aspect of scientific collaboration networks can be reliably tracked by analysing co-authorship networks by bibliometric methods In the present study, scientific collaboration is considered both at individual and national levels, with special focus given to multinational collaborations Both literature data and original results witnessed a dramatic quantitative and structural change in the last decades of the 20th century The changes, to great extent, can be attributed to the universal tendencies of globalisation and the political restructuring of Europe The standards and, particularly, the visibility of scientific research, as a rule, benefit from the ever increasing level of collaboration, but the profits do not come automatically This fact underlines the necessity of a regular quantitative monitoring of inputs and outcomes, ie, bibliometric surveys

690 citations

Journal ArticleDOI
TL;DR: It is suggested that a h-type index - equal to h if you have published h papers, each of which has at least h citations - would be a useful supplement to journal impact factors.
Abstract: We suggest that a h-type index - equal to h if you have published h papers, each of which has at least h citations - would be a useful supplement to journal impact factors.

661 citations

Journal ArticleDOI
TL;DR: In this paper, relative indicators of chemistry research in 25 countries are presented as an illustrative example, and the use of relational charts in displaying the indicators broadens the scope of such assessments.
Abstract: Cross-field comparison ofscientometric indicators 1 is severely hindered by the differences in publication and citation habits of science fields. However, relating publication and citation indicators to proper field-specific reference standards,relative indicators can be built, which may prove rather useful in the comparative assessment of scientists, groups, institutions or countries. The use ofrelational charts in displaying the indicators broadens the scope of such assessments. Relative indicators of chemistry research in 25 countries are presented as an illustrative example.

462 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

18,415 citations

Journal ArticleDOI
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

Journal ArticleDOI
TL;DR: The major concepts and results recently achieved in the study of the structure and dynamics of complex networks are reviewed, and the relevant applications of these ideas in many different disciplines are summarized, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

9,441 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

Journal IssueDOI
TL;DR: Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
Abstract: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. © 2007 Wiley Periodicals, Inc.

4,181 citations