Estimating the size of personal networks

Killworth, Peter D.; Johnsen, Eugene C.; Bernard, H. Russell; Shelley, Gene A.; McCarty, Christopher

doi:10.1016/0378-8733(90)90012-x

Cited by 239 publications

(95 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This number is right-censored because of space limitations on the survey. Research suggests that people may hold hundreds of close and distant associations (Killworth et al 1990); however, these are difficult to capture in survey work. Writing out names produces subject fatigue, and this generally limits the number listed.…”

Section: Methods Setting Respondents and Samplementioning

confidence: 99%

Women's and Men's Career Referents: How Gender Composition and Comparison Level Shape Career Expectations

Gibson

Lawrence

2010

Organization Science

View full text Add to dashboard Cite

This study examines how women's and men's career referents—the people they see as having similar careers—affect career expectations. We raise two questions. First, what is the relative effect of the gender composition and comparison level of career referents on such expectations? Second, what happens to career expectations when women and men identify career referents at the same comparison level? Current research suggests that women have lower career expectations than men because they compare themselves with women who hold lower-level positions than the career referents identified by men. Thus, if women and men identify with career referents at a similar level, their career expectations should be equal. However, this chain of reasoning has not been tested. Using data collected from a large organization, we identify both the specific individuals that women and men perceive as having similar careers and these referents' career levels, defined as their hierarchical level in the firm. The results show that the level of career referents is more important than their gender composition in explaining individuals' career expectations. In contrast to extant explanations, the results show that even when women identify career referents at the same levels as men do, they still exhibit significantly lower career expectations. Drawing on social comparison theory, we speculate that this occurs because men's expectations are bolstered by extreme upward comparisons, whereas women's expectations are dampened, perhaps because they see high-achieving others as representing a less probable goal.

show abstract

Section: Methods Setting Respondents and Samplementioning

confidence: 99%

Women's and Men's Career Referents: How Gender Composition and Comparison Level Shape Career Expectations

Gibson

Lawrence

2010

Organization Science

View full text Add to dashboard Cite

show abstract

“…The group-specific gregariousness for members of group G k reflects an association previously noted in the scientific domains where ARD are often used. Politicians or clergy members typically have larger than average networks, for example, while members of some heavily stigmatized or hard-to-reach groups tend to have smaller than average networks (Killworth et al, 1990). Let N k be the number of members of subpopulation k and N be the total population size.…”

Section: Latent Surface Model For Ardmentioning

confidence: 99%

Latent Surface Models for Networks Using Aggregated Relational Data

McCormick

Tian

2015

Journal of the American Statistical Association

View full text Add to dashboard Cite

Despite increased interest across a range of scientific applications in modeling and understanding social network structure, collecting complete network data remains logistically and financially challenging, especially in the social sciences. This paper introduces a latent surface representation of social network structure for partially observed network data. We derive a multivariate measure of expected (latent) distance between an observed actor and unobserved actors with given features. We also draw novel parallels between our work and dependent data in spatial and ecological statistics. We demonstrate the contribution of our model using a random digit-dial telephone survey and a multiyear prospective study of the relationship between network structure and the spread of infectious disease.The model proposed here is related to previous network models which represents high dimensional structure through a projection to a low-dimensional latent geometric surface-encoding dependence as distance in the space. We develop a latent surface model for cases when complete network data are unavailable. We focus specifically on Aggregated Relational Data (ARD) which measure network structure indirectly * The authors appreciate the support of the Columbia Applied Statistics Center, the Columbia Population Research Center, and the Statistical and Applied Mathematical Sciences Institute (SAMSI), NSF SES 1023176, USARO 62389-CS-YIP and a Google Faculty Award. We also thank Peter Killworth, Russell Bernard, Chris McCarty, two referees, an Associate Editor, and the Editor.1 by asking respondents how many connections they have with members of a certain subpopulation (e.g. How many individuals do you know who are HIV positive?) and are easily added to existing surveys. Instead of conditioning on the (latent) distance between two members of the network, the latent surface model for ARD conditions on the expected distance between a survey respondent and the center of a subpopulation on a latent manifold surface. A spherical latent surface and angular distance across the sphere's surface facilitate tractable computation of this expectation. This model estimates relative homogeneity between groups in the population and variation in the propensity for interaction between respondents and group members. The model also estimates features of groups which are difficult to reach using standard surveys (the homeless, for example).

show abstract

“…Interactions between actors and offline communications between persons have always been central in the study of social networks [30] [31] [32]. Many studies investigated ties between friends and relatives in order to understand why actors provide different types of social support [30], how social networks are formed, persist and disappear [31], and what methods are appropriate in order to estimate the size of personal communication networks [32].…”

Section: Online Social Networkmentioning

confidence: 99%

“…Many studies investigated ties between friends and relatives in order to understand why actors provide different types of social support [30], how social networks are formed, persist and disappear [31], and what methods are appropriate in order to estimate the size of personal communication networks [32]. More recently, the impact of social-based technologies on users, and particularly the influence of online social networks, is becoming the major source of contemporary fascination and controversy [27] [33] [34].…”

Section: Online Social Networkmentioning

confidence: 99%

Privacy in Online Social Networks

Raad

Chbeir

2013

Lecture Notes in Social Networks

View full text Add to dashboard Cite

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.

show abstract

Estimating the size of personal networks

Cited by 239 publications

References 6 publications

Women's and Men's Career Referents: How Gender Composition and Comparison Level Shape Career Expectations

Women's and Men's Career Referents: How Gender Composition and Comparison Level Shape Career Expectations

Latent Surface Models for Networks Using Aggregated Relational Data

Privacy in Online Social Networks

Contact Info

Product

Resources

About