Business-oriented Analysis of a Social Network of University Students

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.Social Networks; Business Science; Cluster Analysis; Community Detection; Community Comparison; Individual Data; Relational Data

Detection and Interpretation of Communities in Complex Networks: Methods and Practical Application

Community detection is an important part of network analysis and has become a very popular field of research. This activity resulted in a profusion of community detection algorithms, all different in some not always clearly defined sense. This makes it very difficult to select an appropriate tool when facing the concrete task of having to identify and interpret groups of nodes, relatively to a system of interest. In this article, we tackle this problem in a very practical way, from the user's point of view. We first review community detection algorithms and characterize them in terms of the nature of the communities they detect. We then focus on the methodological tools one can use to analyze the obtained community structure, both in terms of topological features and nodal attributes. To be as concrete as possible, we use a real-world social network to illustrate the application of the presented tools, and give examples of interpretation of their results from a Business Science perspective.Complex Networks, Community detection, Business Science, Community interpretation

DOI: 10.1109/ASONAM.2010.15 Business-oriented Analysis of a Social Network of University Students Informative value of individual and relational data compared through group detection

Abstract — 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 attribute

Valeur informative supplémentaire apportée par l'analyse des réseaux sociaux: Premiers enseignements d'une étude au sein d'une population étudiante

Social networks analysis is particularly promising from a theoretical point of view, but it is also very difficult to set up, which restricts its practical use. Consequently, people sometimes try to limit group analysis to cluster study only. On the contrary, our approach consists in comparing information extracted using both approaches. We first perform a cluster analysis based on attributes of individuals belonging to a given group, and we then analyze the structure of the social network underlying the same group. A study was conducted among students in the Galatasaray University in Istanbul, Turkey, to gather data. After having applied our method, we find strong complementarities of the information revealed by each kind of analysis in terms of depth and managerial implications.Si l’analyse des réseaux sociaux semble particulièrement fertile d’un point de vue théorique, les difficultés liées à sa mise en place pratique en restreignent actuellement l’utilisation concrète. Aussi, est-on parfois tenté de limiter l’analyse d’un groupe en effectuant seulement une étude typologique. Le but de notre étude est de comparer l’information à laquelle aboutissent les deux approches : une analyse typologique basée sur les attributs des individus appartenant à un groupe et une analyse de la structure des réseaux sociaux au sein de ce groupe. A cette fin, une recherche a été réalisée auprès des étudiants de l’université Galatasaray à Istanbul, Turquie. Elle conclut à une forte complémentarité de l’information révélée par chacune des approches en termes de richesse et d’implications managériales

Représentations sociales de la publicité : résultats et perspectives d'une étude comparative

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