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] Replika 29 139 150 Amblard, F. (2003): Simultaning Social Networks: A Review of Three Books. JASSS

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Longer-term seeding effects on epidemic processes: a network approach

A korai fertőzések helyzetének hosszú távú hatásai a járványterjedésre: egy hálózati megközelítés

Scientia et Securitas
Authors:
Gergely Ódor
,
Domonkos Czifra
,
Júlia Komjáthy
,
László Lovász
, and
Márton Karsai

processes on complex networks. Cambridge University Press. 3 Barthelemy, M. (2011) Spatial networks. Physics Reports, Vol. 499. No. 1–3. pp. 1

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Social Networks: Analysis of Key Features and Prediction of Tie Strength in Facebook. Computer Communications , 36 , 1130–1144. Passarella A. Egocentric Online Social Networks

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Scientometrics
Authors:
Katy Börner
,
Weixia Huang
,
Micah Linnemeier
,
Russell Duhon
,
Patrick Phillips
,
Nianli Ma
,
Angela Zoss
,
Hanning Guo
, and
Mark Price

Abstract  

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this ‘BIG science of science’—also called ‘computational scientometrics’ research—in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) (http://sdb.slis.indiana.edu), which provides free online access to 22 million scholarly records—papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool (http://nwb.slis.indiana.edu). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.

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Journal of Behavioral Addictions
Authors:
Maria Picó-Pérez
,
Víctor Costumero
,
Juan Verdejo-Román
,
Natalia Albein-Urios
,
José Miguel Martínez-González
,
Carles Soriano-Mas
,
Alfonso Barrós-Loscertales
, and
Antonio Verdejo-Garcia

learning effects during the long-term exposure to drugs that lead to addiction. Current research shows that alterations related to CUD may be understood in terms of large-scale brain networks dysfunction ( Zilverstand, Huang, Alia

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-based approaches in which the topics of diffusion can be tracked were discussed by Chen and Hicks ( 2004 ). Particularly, they introduced an approach that combined complex network theory, network visualization, and patent citation analysis to describe knowledge

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Introduction Informetrics is not only the study of regularities (the so-called informetric laws) or of citation counting and its consequences; it is also the study of related algebraic structures such as graphs or networks. In

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2002 ). Commercial bibliometric databases and repositories of open access journals have contributed to network analyses by offering citation indexes and data on co-author relationships. Typical applications of the results of network analyses of

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Abstract  

Many investigations of scientific collaboration are based on statistical analyses of large 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 illustrate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a relatively small network of scientific collaboration (N = 291) constructed from the bibliographic record of a research centerin the development and application of wireless and 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 assortative mixing of selected node characteristics, unveiling the researchers’ propensity to collaborate preferentially with others with a similar academic profile. 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.

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