Estimating network properties from snowball sampled data

Illenberger, Johannes; Flötteröd, Gunnar

doi:10.1016/j.socnet.2012.09.001

Cited by 60 publications

(46 citation statements)

References 20 publications

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“…To provide test cases, we exploited an anonymized data set that was gathered from Facebook [13]. We selected 10 sizes ranging from 400 to 2500 people, and for each size value we randomly extracted 10 sub-graphs from the data set using the snowball sampling algorithm [14], which has been shown to preserve the topological structure of graphs. For each extracted sub-graph, we created a KB (in RDF) containing nodes of type Person and mse:knows relationships between them.…”

Section: B Experimental Results On Smart Phonesmentioning

confidence: 99%

Policy-Based Access Control in Mobile Social Ecosystems

Hachem

Toninelli

Pathak

et al. 2011

2011 IEEE International Symposium on Policies for Distributed Systems and Networks

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Abstract-The novel scenarios enabled by emerging mobile social applications raise serious concerns regarding access control of users' contextual and social data. Given the variety of existing and upcoming social applications, it is important to provide (i) generic yet flexible policy models that combine expressivity with personalization, (ii) actual running infrastructures to enforce policy-based access control on heterogenous devices with minimal development/deployment effort, and (iii) user-interfaces to allow the easy specification of policies without dealing with the complexity of the underlying policy and data models. Toward this goal, in this paper we make three contributions. First, we present a novel policy framework for controlling access to social data in mobile applications. The framework allows the representation of expressive policies based on users' social interactions, which can be easily extended with new domain data models, while keeping policy model compatibility intact. Secondly, we demonstrate how we integrated the policy framework as part of Yarta, a middleware for managing mobile users' social ecosystems, implemented and deployed on laptops and smart phones. Third, we show the graphical policy editor provided with the policy framework to allow non-technology savvy users to easily specify and manage their access control policies.

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Section: B Experimental Results On Smart Phonesmentioning

confidence: 99%

Policy-Based Access Control in Mobile Social Ecosystems

Hachem

Toninelli

Pathak

et al. 2011

2011 IEEE International Symposium on Policies for Distributed Systems and Networks

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“…Recruiting 40 seeds to start the snowball chains in two consecutive sub-samples with 20 seeds each helped to balance homophily bias. In addition the project team in Berlin developed a method to balance degree-bias (Illenberger and Flötteröd 2012). …”

Section: Discussionmentioning

confidence: 99%

“…Chains were started with 40 seeds. Surveying properties of a population-wide network by starting snowball chains with few initial respondents, which are referred to as 'ego-seeds', and continuing for a medium number of iterations was suggested by a simulation study at TU Berlin (Illenberger and Flötteröd 2012). Continuing with seeds" social contacts, the study collected information on nearly 800 respondents and their approximately 15,600 social contacts.…”

Section: Illenberger and Flötteröd 2012)mentioning

confidence: 99%

Social Networks and Travel Behaviour

Kowald¹,

Axhausen²

2016

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“…Additionally, if strong homophily exists between individuals, the researcher is confronted with a homogenous cluster. (Illenberger& Flötteröd, 2012). As this situation is given for the target group of students in the DACH region, the snowball principle was applied in the data collection procedure.…”

Section: Methodsmentioning

confidence: 99%

“…(Gruman and Saks, 2011) From the methodological point of view a different sampling may reveal different results. Various methods to account for snowball sampling bias have been proposed in the past (Frank & Snijders, 1994;Thompson & Frank, 2000;Heckathorn, 2002;Chow and Thompson, 2003;Volz & Heckathorn, 2008;Handcock & Gille, 2010) Since snowball sampling can be implemented in quite different variants, each specification usually requires its own inference approach (Illenberger & Flötteröd, 2012). Further research should therefore focus on the effects on variations in different sampling designs.…”

Section: Implications For Researchmentioning

confidence: 99%

Personality Differences in Organisational Socialisation Tactics

Burböck

Schnepf

Pessl

2014

Journal of Intercultural Management

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Abstract:The demographic shifts, the increased workforce mobility, and the shortage of skilled workers have led to a new thinking within human resource management. To address this issue, organisations adapt their methods towards a higher focus on their employees. The purpose of this paper is therefore the identification of organisational socialisation tactics, which are classified into the individualized socialisation strategy and the institutionalised socialisation strategy. Studies show that the institutionalised socialisation strategy causes better results in terms of newcomer adjustment than the individualized socialisation strategy. In this context, the instiutionalised socialization strategy determines that organisations consider newcomers' personalities and demographic characteristics. The results of this paper support this argument and reveal that students in the DACH region differ in their organisational socialisation preferences. In other words, a well-structured organisational socialisation process, which considers newcomers' personalities can lead to a competitive advantage for organisations.

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Estimating network properties from snowball sampled data

Cited by 60 publications

References 20 publications

Policy-Based Access Control in Mobile Social Ecosystems

Policy-Based Access Control in Mobile Social Ecosystems

Social Networks and Travel Behaviour

Personality Differences in Organisational Socialisation Tactics

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