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

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 seem to 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.