The School Child’s Choice of Companions

Wellman, Beth L.

doi:10.1080/00220671.1926.10879675

Cited by 76 publications

(27 citation statements)

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“…The quantitative analysis of social interactions goes back to the early 1920s [513]. The study of the school child's choice of companions reported by Wellman in 1926 [514] and the Mayo's study of social interactions between factory workers in the 1930s [19] are some examples of pioneering works in social network analysis. The cross-interaction of researchers form diverse disciplines as sociology, applied anthropology, social psychology and statistics has raised over the years a solid scientific discipline with its own textbooks [18,19], and specialized journals as Social Networks published by Elsevier.…”

Section: Social Networkmentioning

confidence: 99%

Complex networks: Structure and dynamics

et al. 2006

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Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks' dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

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Section: Social Networkmentioning

confidence: 99%

Complex networks: Structure and dynamics

et al. 2006

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“…Among groups with similar demographical attributes, homophilous behavior can be observed (McPherson and Smith-Lovin 1987). The argument of homophilous behavior is derived from the concept of social similarity (McPherson et al 2001;Wellman 1926)-meaning that individuals are acting in favor of other individuals that are similar to themselves (Pearce and Xu 2012). The underlying argument is the individuals' expectation of the similarity of values, interests and norms of individuals with similar demographical characteristics (Ibarra 1992;McPherson et al 2001).…”

Section: Network Embeddedness Effects On Discriminationmentioning

confidence: 99%

Beyond human capital explanations for the gender pay gap among executives: investigating board embeddedness effects on discrimination

Oehmichen

Sarry

Wolff

2014

Bus Res

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This paper examines the gender pay gap in top management teams and how it is affected by directors’ embeddedness. We can reconfirm the result of previous studies that differences in managerial compensation between women and men exist, even after controlling for company properties and human capital attributes. Drawing on the language theory of discrimination, we then question how the embeddedness of directors—the actual deciders on executive compensation levels—affects the pay gap: embeddedness causing homophilous behavior resulting in a higher pay gap or directors using their network as knowledge source and decreasing the pay gap. We find evidence that knowledge effects outweigh potential homophily effects.

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“…and social ties (e.g., influence, interaction, companionship, etc.) [104] [105] [106]. Drawing inspiration from these previous works, modern social network analysis emerged as interdisciplinary field with contributions from various areas of study such as sociology, anthropology, and mathematics [107] [108].…”

Section: Development and Measuresmentioning

confidence: 99%

Privacy in Online Social Networks

Raad

Chbeir

2013

Lecture Notes in Social Networks

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Abstract. Online social networks have become an important part of the online activities on the web and one of the most influencing media. Unconstrained by physical spaces, online social networks offer to web users new interesting means to communicate, interact, and socialize. While these networks make frequent data sharing and inter-user communications instantly possible, privacy-related issues are their obvious much discussed immediate consequences. Although the notion of privacy may take different forms, the ultimate challenge is how to prevent privacy invasion when much personal information is available. In this context, we address privacy-related issues by resorting to social network analysis and link mining techniques. We first describe the fundamental of social networks, their common representations, and the main motivations associated with their use. Afterwards, we particularly show how privacy attacks can build on social network analysis and link mining techniques to reveal user-sensitive information. The chapter concludes with a discussion of some open challenges to address in future privacy-related works.

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The School Child’s Choice of Companions

Cited by 76 publications

References 0 publications

Complex networks: Structure and dynamics

Complex networks: Structure and dynamics

Beyond human capital explanations for the gender pay gap among executives: investigating board embeddedness effects on discrimination

Privacy in Online Social Networks

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