The Experts below are selected from a list of 198213 Experts worldwide ranked by ideXlab platform
Kevin J Dooley - One of the best experts on this subject based on the ideXlab platform.
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structural investigation of supply Networks a Social Network Analysis approach
Journal of Operations Management, 2011Co-Authors: Thomas Y Choi, Kevin J DooleyAbstract:Abstract A system of interconnected buyers and suppliers is better modeled as a Network than as a linear chain. In this paper we demonstrate how to use Social Network Analysis to investigate the structural characteristics of supply Networks. Our theoretical framework relates key Social Network Analysis metrics to supply Network constructs. We apply this framework to the three automotive supply Networks reported in Choi and Hong (2002) . Each of the supply Networks is analyzed in terms of both materials flow and contractual relationships. We compare the Social Network Analysis results with the case-based interpretations in Choi and Hong (2002) and conclude that our framework can both supplement and complement case-based Analysis of supply Networks.
Yu Fan - One of the best experts on this subject based on the ideXlab platform.
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Research on Application Model of Semantic Web-based Social Network Analysis
Information Sciences, 2011Co-Authors: Yu FanAbstract:The extensive research of semantic web technology provides a new way of studying on Social Network Analysis methods,which has become the focus of Social Network Analysis area.This paper takes the application of semantic web technology in Social Network Analysis field as its object.First,the article reviews the achievement on the semantic web and Social Network Analysis achieved by both domestic and foreign scholars.Second the paper makes an Analysis on the Social semantic Networks,and proposes an application model Semantic Web-based Social Network Analysis.The paper puts forward technical difficulty in the course of developing of the model as well as its future research field in the end.
Michel Buffa - One of the best experts on this subject based on the ideXlab platform.
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Semantic Social Network Analysis
2009Co-Authors: Guillaume Erétéo, Fabien Gandon, Olivier Corby, Michel BuffaAbstract:Social Network Analysis (SNA) has been widely studied since the middle of the 20th century. Research in this field tries to understand and exploit the key features of Social Networks in order to manage their life cycle and predict their evolutions. Detecting strategic positions, roles and communities are among its main concerns. The increasingly popular web 2.0 sites form the largest Social Network. Some researchers apply classical methods from Social Network Analysis (SNA) to such online Networks; others provide models to leverage the semantics of their representation. In this paper, we propose to leverage semantic web technologies to merge and exploit the best features of each of these approaches. Furthermore, we show how to facilitate and enhance the Analysis of online Social Networks, exploiting the power of semantic Social Network Analysis.
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Semantic Social Network Analysis
Proceedings of the Web Science, WebSci 2009, 2001Co-Authors: Guillaume Erétéo, Fabien Gandon, Olivier Corby, Sophia Antipolis, Michel BuffaAbstract:Social Network Analysis (SNA) tries to understand and exploit the key features of Social Networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge Social Network. Classical methods from Social Network Analysis (SNA) have been applied to such online Networks. In this paper, we propose leveraging semantic web technologies to merge and exploit the best features of each domain. We present how to facilitate and enhance the Analysis of online Social Networks, exploiting the power of semantic Social Network Analysis.
Yoshiaki Matsuzawa - One of the best experts on this subject based on the ideXlab platform.
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knowledge building discourse explorer a Social Network Analysis application for knowledge building discourse
Educational Technology Research and Development, 2012Co-Authors: Jun Oshima, Ritsuko Oshima, Yoshiaki MatsuzawaAbstract:In recent studies of learning theories, a new methodology that integrates two prevailing metaphors of learning (acquisition and participation) has been discussed. However, current analytical techniques are insufficient for analyzing how Social knowledge develops through learners' discourse and how individual learners contribute to this development. In this paper, we propose a novel approach to analyzing learning from an integrative perspective and present a Social Network Analysis application that uses learner discourse as input data: Knowledge Building Discourse Explorer (KBDeX). To investigate the utility of this approach, discourse data analyzed in a previous study is re-examined through Social Network Analysis supported by KBDeX. Results suggest that Social Network Analysis can qualitatively and quantitatively support the conclusions from the previous study. In addition, Social Network Analysis can reveal potential points that are pivotal for Social knowledge advancement in groups, and can identify each individual's contribution to this advancement. On the basis of these results, we discuss how Social Network Analysis could be integrated into existing in-depth discourse Analysis.
John Scott - One of the best experts on this subject based on the ideXlab platform.
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The SAGE Handbook of Social Network Analysis - The SAGE Handbook of Social Network Analysis
2014Co-Authors: John Scott, Peter J. CarringtonAbstract:Social Network Analysis has been one of the fastest growing and most influential areas of recent times. This sparkling Handbook offers an unrivalled resource. Systematically, it introduces readers to the key concepts, substantive topics, central methods, and prime debates. The result is a peerless resource for teachers and students. Instead of consulting a variety of books and journal articles, the Handbook offers a one-stop guide that will be used by readers for decades to come.
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Social Network Analysis a handbook
2000Co-Authors: John ScottAbstract:Networks and Relations The Development of Social Network Analysis Handling Relational Data Lines, Direction and Density Centrality and Centralization Components, Cores, and Cliques Positions, Roles, and Clusters Dimensions and Displays Appendix Social Network Packages
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Social Network Analysis
2000Co-Authors: John ScottAbstract:This paper reports on the development of Social Network Analysis, tracing its origins in classical sociology and its more recent formulation in Social scientific and mathematical work. It is argued that the concept of Social Network provides a powerful model for Social structure, and that a number of important formal methods of Social Network Analysis can be discerned. Social Network Analysis has been used in studies of kinship structure, Social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. A review of the formal models proposed in graph theory, multidimensional scaling, and algebraic topology is followed by extended illustrations of Social Network Analysis in the study of community structure and interlocking directorships.
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Social Network Analysis: A Handbook By John Scott
2000Co-Authors: John ScottAbstract:If you are searching for a ebook by John Scott Social Network Analysis: A Handbook in pdf format, then you've come to correct website. We presented the complete version of this ebook in DjVu, txt, ePub, doc, PDF forms. You can read by John Scott online Social Network Analysis: A Handbook either downloading. In addition to this ebook, on our website you can reading the instructions and other artistic books online, or load their. We want attract your note that our site not store the eBook itself, but we provide link to site whereat you may download or reading online. So that if have must to download pdf Social Network Analysis: A Handbook by John Scott , then you have come on to the loyal website. We own Social Network Analysis: A Handbook doc, txt, PDF, ePub, DjVu forms. We will be glad if you revert more.
