Understanding complex interactions using social network analysis

Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

doi:10.1111/j.1365-2702.2011.04036.x

Cited by 41 publications

(28 citation statements)

References 36 publications

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“…As has been stated (Pow, Gayen, Elliott, & Raeside, 2012), SNA maps assist us in understanding the interactions within a group, namely the links between diff erent players and the fl ow of information in the system. Th is method has been used in various fi elds of research including sport (e.g., Cotta, Mora, Merelo, & Merelo-Molina, 2013;Fransen et al, 2015), usually to expose the relationships between diff erent elements (i.e., persons) of a given system.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition

Loureiro¹,

Hurst²,

Valongo³

et al. 2017

Monten. J. Sports Sci. Med.

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A deeper understanding of the factors behind performance and their interactions is essential to promote better training practices. Notwithstanding, the focus often relies on the outcomes of players' actions (e.g., effi cacy rates), whereas the nature and variations of particular classes of actions remain largely unexplored. Our purpose was to conduct a systemic analysis of categorical game variables and their interactions using Social Network Analysis. Game actions were counted as nodes and their interactions as edges. Eigenvector centrality values were calculated for each node. Eight matches of the Men's World Cup 2015 were analysed, composing a total of 27 sets (1,209 rallies). Four game complexes were considered: Complex 0 (Serve), Complex I (Side-out), Complex II (Side-out transition) and Complex III (Transition). Results showed that teams frequently play in-system when in Complex I (i.e. under ideal conditions), but present reduced variation with regard to attack zones and tempos, whereas in Complex II teams most often play out-of-system. Based on these fi ndings, it was concluded that practicing with non-ideal conditions is paramount for good performance in Complex II. Furthermore, most literature combines Complex II and Complex III as a single unit (counter-attack); however, our results reinforce the notion that these two game complexes differ and should be analysed separately.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition

Loureiro¹,

Hurst²,

Valongo³

et al. 2017

Monten. J. Sports Sci. Med.

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“…Applying SNA to nursing education provides a simple method that helps to gain an understanding of human interaction. 18 One important feature of SNA is that the social network relations can be visualized as graphs, which are called sociograms. The sociograms represent the actors as nodes in a graph and the interactions as linking lines among nodes.…”

Section: Social Network Analysismentioning

confidence: 99%

Using Social Network Analysis to Sketch the Patterns of Interaction Among Nursing Students in a Blog Community

Lin

2013

CIN: Computers, Informatics, Nursing

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Web logs, or blogs have recently raised interest in the educational and research arena. However, the analysis of participants' behavioral patterns remains absent. The aim of this study was to use social network analysis to draw the patterns of 48 junior college nursing students' peer interactions in a blog community in Taiwan. The results showed that the density of interactions were different in terms of the participants with different grade-point-average background. The more active students in the blog community dominated discourse interaction. Nursing students with higher grade point average interacted mainly within their respective groups. The social network analysis helped to provide information on patterns of nursing students' interaction culture within a blog community.

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“…In related research literature, Tichy et al [24] applies SNA to organizations, while Johnson et al [12] uses social networks to study friendship and communication structure between groups of people working in the same bank by analyzing email trafficking data. Pow et al [20] suggests usage of SNA in nursing to analyze why some people are open to organizational changes while the others are not. Similarly, SNA is used and applied further by many other authors such as Quinn et al [21], Mainas [13], Oshima et al [19] and Cserpes [7].…”

Section: Introductionmentioning

confidence: 99%

Social network analysis of scientific collaborations across different subject fields

Bozdogan

Akbilgiç

2013

ISU

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This paper analyzes the level of scientific collaboration and interaction in different subject fields using a novel social network analysis (SNA) on a data set provided by the Department of Energy (DOE) Office of Scientific and Technologic Information (OSTI) in Oak Ridge, Tennessee. This paper not only determines the level of scientific collaboration between different disciplines, but it also analyzes the trends among the subject fields considered. The results in this paper show clear pattern recognition discovery on the social network model among key subject fields using weighted networks.

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Understanding complex interactions using social network analysis

Cited by 41 publications

References 36 publications

A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition

A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition

Using Social Network Analysis to Sketch the Patterns of Interaction Among Nursing Students in a Blog Community

Social network analysis of scientific collaborations across different subject fields

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