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measures of peer effects, which revealed inconsistent findings. A recent narrative review ( Hoeben, Meldrum, Walker, & Young, 2016 ) suggested that future studies should use a social network analysis (SNA) to measure objective peer effects, while avoiding

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is still needed to understand the gap between collaborative tagging system users and IO professionals. This article uses social network analysis (hereafter SNA) and the frequent-pattern tree (hereafter FT tree) method to identify the patterns

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Scientometrics
Authors: Zaida Chinchilla-Rodríguez, Anuska Ferligoj, Sandra Miguel, Luka Kronegger, and Félix de Moya-Anegón

analysis and social network analysis to establish new perspectives for analysing scientific collaboration networks and monitoring individual and group careers. Data and methods The methodology combines three approaches. Two

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researchers (e.g., citation analysis and co-authorship analysis) (Hou et al. 2008 ; Lu and Feng 2009 ; Newman 2001a ; Park et al. 2005 ; Park and Leydesdorff 2008 ; Pritchard 1969 ). However, in this article, we used social network analysis

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directly reveal the fronts or academic commanding points in the development and evolution of knowledge, which are widespreadly paid attention to (Liu et al. 2008 ). The focus of social network analysis (SNA) is on the relationships among social

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Social network analysis Previous studies on scientific communities or on knowledge diffusion in large have either focused on co-authorship relations or semantic relations. As mentioned in Sect. 2 , there are a few number of studies which have

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Summary  

The use of electronic data is steadily gaining ground in the study of the social organization of scientific and research communities, decreasing the researcher's reliance on commercial databases of bibliographic entries, patents grants and other manually constructed records of scientific works. In our work we provide a methodological innovation based on semantic technology for dealing with heterogeneity in electronic data sources. We demonstrate the use of our electronic system for data collection and aggregation through a study of the Semantic Web research community. Using methods of network analysis, we confirm the effect of Structural Holes and provide novel explanations of scientific performance based on cognitive diversity in social networks.

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social network analysis (SNA) to identify trends from word co-occurrences (Lee and Jeong 2008 ; Callon et al. 1991 ). However, to identify occurrences of keywords, CWA should define in advance a set of keyword or key phrase patterns which are

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Abstract  

Patents are important intellectual assets for companies to defend or to claim their technological rights. To control R&D cost, companies should carefully examine their patents by patent quality. Approaches to evaluating patent quality are mostly a posteriori uses of factual information of patent quality. This paper examined whether patent quality can be predicted a priori, i.e., during the early years after a patent is granted, by analyzing information embedded in a network of patent citations. Social network analysis was applied to analyze two network positions occupied by a patent, brokerage and closure to determine whether either position is a good predictor of patent quality. Patent renewal decisions and forward citations were adopted as surrogates of patent quality. The analytical results showed that forward citations can be positively predicted by the brokerage position and negatively predicted by the closure position in the early and mature stages. Renewal decisions can be negatively predicted by the brokerage position in the early stage, and the closure position influences the renewal decision in a different way in the early and mature stages. These analytical results imply that a company should focus on developing patents that bridge different technologies as its technological developments reach maturity.

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Abstract  

Based on a face-to-face survey of 312 scientists from government research institutes and state universities in two Philippine locations — Los Baños, Laguna and Muñoz, Nueva Ecija — we examine how graduate training and digital factors shape the professional network of scientists at the “Global South.” Results suggest that scientists prefer face-to-face interaction; there is no compelling evidence that digitally-mediated interaction will replace meaningful face-to-face interaction. What is evident is that among none face-to-face modes of communication a reordering maybe in progress. The effect of digital factors — expressed through advance hardware-software-user interaction skills — lies on network features pertaining to size, proportion of male and of core-based alters, and locational diversity. International graduate training and ascribed factors (gender and number of children) also configure the professional network of scientists — actors traditionally viewed as the epitome of rationality and objectivity. We argue that these factors influence knowledge production through a system of patronage and a culture that celebrates patrifocality. We forward the hypothesis that knowledge production at the “Global South” closely fits Callon’s [1995] extended translation model of science.

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