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A Brief Overview of Social Network Analysis and its Current State within Romanian Sociology

. Molina (2003) ‘Ethical and Strategic Issues in Organizational Social Network Analysis’. The Journal of Applied Behavioral Science, 39(3): 337-349. Borgatti, S.P. and P.C. Foster (2003) ‘The Network Paradigm in Organizational Research: A Review and Typology’. Journal of Management, 29(6): 991-1013 Brass, D. J., D. M. Krackhardt (2012) ‘Power, Politics, and Social Networks in Organizations’. In G. R. Ferris and D. C. Treadway (eds.) Politics in Organizations. Theory and Research Considerations, pp. 355-375. New York & London: Routledge, Taylor and

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An overview of Software Applications for Social Network Analysis


There is a great variety of software tools that has been developed within the last 20 years, as to facilitate and support the qualitative and quantitative analysis of social networks. This paper gives a brief overview of some of the most popular software packages for social network analysis: Pajek, UCINET 6, NetDraw, Gephi, E-Net, KeyPlayer 1, StOCNET and Automap. Pajek has efficient algorithms for the analysis of large networks, while UCINET 6 includes multiple analytical tools highly efficient for exploring and measuring social network structures. NetDraw, nested in UCINET 6, and Gephi allow network visualization. E-Net and KeyPlayer 1 satisfy rather specific and well-oriented purposes: ego-network analysis and network key-player operations (node removal or utilization). StOCNET provides a platform for statistical methods focusing on probabilistic models, while Automap is a text mining tool for analyzing text relational data.

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Analysis of Self-Organising Networks of Communication Between the Participants of a Housing Complex Construction Project

.8 (2006): 638-649. [4] Hanisch, Bastian, and Andreas Wald. “A project management research framework integrating multiple theoretical perspectives and influencing factors.” Project Management Journal 42.3 (2011): 4-22. [5] Moreno, Jacob Levy. “Who shall survive?: A new approach to the problem of human interrelations.” (1934). [6] Scott, John. Social network analysis . Sage, 2017. [7] Wasserman, Stanley, and Katherine Faust. Social network analysis: Methods and applications . Vol. 8. Cambridge university press, 1994. [8] Loosemore, Martin

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Managing Information Flow in Self-Organising Networks of Communication Between Construction Project Participants

integrating multiple theoretical perspectives and influencing factors.” Project Management Journal 42.3 (2011): 4-22. [5] Moreno, Jacob Levy. “Who shall survive?: A new approach to the problem of human interrelations.” (1934). [6] Loosemore, Martin. “Construction crises as periods of social adjustment.” Journal of management in engineering 13.4 (1997): 30-37. [7] Loosemore, Martin. “Social network analysis: using a quantitative tool within an interpretative context to explore the management of construction crises.” Engineering, construction and

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Social Network Mixing Patterns In Mergers & Acquisitions - A Simulation Experiment

. (2001). HR Issues and Activities in Mergers and Acquisitions. European Management Journal , 19 (3): 239-253. Vestring, T., King, B., Rouse, T. and Critchlow, J. (2003). Merger integration: why the ‘soft issues’ matter mos. EBF , (13): 69-71. Wasserman, S. and Faust, K. (1994). Social Network Analysis; Methods and Applications. Structural analysis in the social sciences. Cambridge University Press. Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks

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A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education

. Retrieved from 11. Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28 (2), 15–21. doi:10.1109/MIS.2013.30. 12. Carlos, A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons. ISBN 978-1-118-01094-5. 13. Carsten, U., Boreau, K., & Stepanyan, K. (2010). Who students interact with? A social network analysis perspective on the use of Twitter in Language Learning. In M. Wolpers, P. Kirschner, M. Scheffel

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Stakeholder Analysis to Support Secondary Norway Spruce (Picea abies (L.) Karst.) Forest Conversion in the Ukrainian Carpathians

.1505/146554816818206122 B anville , C. – L andry , M. – M artel , J.M. – B oulaire , C. (1998): A stakeholder approach to MCDA. Systems Research and Behavioral Science 15: 15–32.<15::AID-SRES179>3.0.CO;2-B B lanc , S. – L ingua , F. – B ioglio , L. – P ensa , R.G. – B run , F. – M osso , A. (2018): Implementing Participatory Processes in Forestry Training Using Social Network Analysis Techniques. Forests 9: 463. C arnus , J.M. – P arrotta , J. et al. (2006): Planted forests and

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Passing Network Analysis of Positional Attack Formations in Handball

Handball Federation (IHF) since 2016. Therefore, studying the interaction patterns of handball teams considering the different playing positions under various attack formations contributes to the understanding of the sport and its actual development. Social network analysis (SNA) has proven successful in the study of ball passing dynamics by breaking down the complexity within the web of interactions between players ( Passos et al., 2011 ). As a match analysis tool, SNA is able to quantify the contribution of individual players to the general interplay as well as

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Analysing social networks in rural development: a gender approach

, S., Everett, M. and Freeman, L. (2002) Ucinet for Windows Software for Social Network Analysis. Harvard, MA: Analytic Technologies. Buciega, A. and Esparcia, J. (2013) ‘Desarrollo, Territorio y Capital Social. Un Análisis a partir de Dinámicas Relacionales en el Desarrollo Rural’. REDES – Revista Hispana para el Análisis de Redes Sociales , Vol. 24 (1), pp. 81-113. Burt, R. (2005) Brokerage and Closure. An Introduction to Social Capital . Oxford, Oxford University Press. Camarero, L. y Sampedro, R. (2008) ‘¿Por qué se van las mujeres? El

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Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

Science (WoS) were renamed and extended to 225 WoS Categories (WCs) (also, a new set of 151 Subject Areas were added, but a higher level of aggregation) ( Leydesdorff, Carley, & Rafols, 2013 ). Thus, we use WCs to detect the disciplinary structure of N&N, further conducting a comparison with conclusions of Porter and Youtie (2009) with the predecessor SCs. Besides analysis from the perspective of the social network analysis of the disciplinary structure of N&N, cluster analysis by employing cliques embedded in Ucinet software has also been conducted in this paper

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