Are the different layers of a social network conveying the same information?
Singapore University of Technology and Design, Singapore, Singapore
2 Aix Marseille Univ, Université de Toulon, CNRS, CPT, Marseille, France
3 Data Science Laboratory, Institute for Scientific Interchange (ISI) Foundation, Torino, Italy
* e-mail: email@example.com
Accepted: 31 August 2018
Published online: 15 September 2018
Comprehensive and quantitative investigations of social theories and phenomena increasingly benefit from the vast breadth of data describing human social relations that is now available within the realm of computational social science. Such data are, however, typically proxies for one of the many interaction layers composing social networks, which can be defined in many ways and are composed of communication of various types (e.g., phone calls, face-to-face communication, etc.). As a result, many studies focus on one single layer, corresponding to the data at hand. Several studies have however shown that these layers are not interchangeable, despite the presence of a certain level of correlation between them. Here, we investigate whether different layers of interactions among individuals lead to similar conclusions with respect to the presence of homophily patterns in a population—homophily represents one of the widest studied phenomenon in social networks. To this aim, we consider a data set describing interactions and links of various nature in a population of Asian students with diverse nationalities, first language and gender. We study homophily patterns, as well as their temporal evolution in each layer of the social network. To facilitate our analysis, we put forward a general method to assess whether the homophily patterns observed in one layer inform us about patterns in another layer. For instance, our study reveals that three network layers—cell phone communications, questionnaires about friendship, and trust relations—lead to similar and consistent results despite some minor discrepancies. The homophily patterns of the co-presence network layer, however, does not yield any meaningful information about other network layers.
Key words: Social networks / Multilayer networks / Temporal homophily
© The Author(s), 2018