Réseaux sociaux numériques et marché du travail

Fondeur, Yannick; Lhermitte, France

doi:10.3917/rdli.052.0101

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Consequently, their analysis has been considered primarily in a utilitarian perspective, with a particular emphasis on their impact on the nature and efficiency of information dissemination. Their role was noticeably studied in the context of competitiveness in the construction sector [2], firm innovativeness [3], investors attraction for venture capital [4], effective use of their social capital [5] in the labor market [6], and administrative boards decision [7], among others. In Marketing Science, the focus has been put more on speed of information diffusion, with a major interest in word-of-mouth [8], changes of opinions and adoption of innovations inside groups of people (mainly consumers and potential consumers) [9][10][11] or diffusion of specific products [12].…”

Section: Introductionmentioning

confidence: 99%

Business-Oriented Analysis of a Social Network of University Students: Informative Value of Individual and Relational Data Compared through Group Detection

Labatut

Balasque

2010

2010 International Conference on Advances in Social Networks Analysis and Mining

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Despites the great interest caused by social networks in Business Science, their analysis is rarely performed both in a global and systematic way in this field: most authors focus on parts of the studied network, or on a few nodes considered individually. This could be explained by the fact that practical extraction of social networks is a difficult and costly task, since the specific relational data it requires are often difficult to access and thereby expensive. One may ask if equivalent information could be extracted from less expensive individual data, i.e. data concerning single individuals instead of several ones. In this work, we try to tackle this problem through group detection. We gather both types of data from a population of students, and estimate groups separately using individual and relational data, leading to sets of clusters and communities, respectively. We found out there is no strong overlapping between them, meaning both types of data do not convey the same information in this specific context, and can therefore be considered as complementary. However, a link, even if weak, exists and appears when we identify the most discriminant attributes relatively to the communities. Implications in Business Science include community prediction using individual data.

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Section: Introductionmentioning

confidence: 99%

Business-Oriented Analysis of a Social Network of University Students: Informative Value of Individual and Relational Data Compared through Group Detection

Labatut

Balasque

2010

2010 International Conference on Advances in Social Networks Analysis and Mining

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“…From 2002, appear the sites "headlights" of "personal sociability" [5] B. Basic structure of the SNS These Community sites all are characterized by an identical basic structure, made up of two essential functionalities:…”

Section: A History Of the Nsnmentioning

confidence: 99%

“…A Social network is a "entity made up of a whole of individuals and relations which they maintain the ones with the others, directly or indirectly by the means of chains of relations" [5]. Yannick Fondeur defines in a more strict way one numerical social network as a "social network formalized via a device based on communication and information technologies and specifically dedicated to the constitution or the reconstitution of social connections, their management and/or their mobilization at personal or professional ends".…”

Section: Social Network Numeralmentioning

confidence: 99%

Digital Social Networks Dedicated to Learning

Ammar¹,

Nader²

2012

IJI

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Abstract-In this paper we analysis the Numerical Social Networks (NSN) devoted to remote teaching which will be devoted to remote teaching We consider only concepts attached to social networking sites, such as the notions of "profile", "friends list" and "groups" can be regarded as positive factors in distance education. We then present an analysis of a sample of these sites and try to highlight different aspects that will support a collaborative, adaptable and scalable training..

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“…Consequently, their analysis has been elaborated primarily in a utilitarian perspective, with a particular emphasis on their impact on the nature and efficiency of information dissemination. Their role was noticeably studied in the context of competitiveness in the construction sector [2], firm innovativeness [3], investors attraction for venture capital [4], effective use of their social capital [5] in the labor market [6], and administrative board decisions [7], among others. In Marketing Science, the focus has been put more on speed of information diffusion, with a major interest in word-of-mouth [8], changes of opinions and adoption of innovations inside groups of people (mainly consumers and potential consumers) [9][10][11] or diffusion of specific products [12].…”

Section: Network Analysis In Business Sciencementioning

confidence: 99%

Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection

Labatut¹,

Balasque²

2012

Lecture Notes in Social Networks

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Despites the great interest caused by social networks in Business Science, their analysis is rarely performed in both a global and systematic way in this field. This could be explained by the fact their practical extraction is a difficult and costly task. One may ask if equivalent information could be retrieved from less expensive, individual data (i.e. describing single individuals instead of pairs). In this work, we try to address this question through group detection. We gather both types of data from a population of students, estimate groups separately using individual and relational data, and obtain sets of clusters and communities, respectively. We measure the overlap between clusters and communities, which turns out to be relatively weak. We also define a predictive model, allowing us to identify the most discriminant attributes for the communities, and to reveal the presence of a tenuous link between the relational and individual data. Our results indicate both types of data convey considerably different information in this specific context, and can therefore be considered as complementary. To emphasize the interest of communities for Business Science, we also conduct an analysis based on hobbies and purchased brands.

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Réseaux sociaux numériques et marché du travail

Cited by 6 publications

References 22 publications

Business-Oriented Analysis of a Social Network of University Students: Informative Value of Individual and Relational Data Compared through Group Detection

Business-Oriented Analysis of a Social Network of University Students: Informative Value of Individual and Relational Data Compared through Group Detection

Digital Social Networks Dedicated to Learning

Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection

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