With the development of the new ICTs and of the forthcoming data deluge, several calls for the epistemological foundation of the new interdisciplinary field of computational social science appeared in major scientific journals [1,2]. In the last decade, several research issues have been successfully addressed, in this context, through the collaboration of sociologists, computer scientists, physicists, etc. The more and more interconnected structure of relational data requires specific in-depth reflection on the epistemological and methodological construction of a more interdisciplinary network science.
According to traditional research paradigms, sociology treats small but rich networks where the richness of the network attributes is derived from the specific build-up of the data collection process. In the sociological approach, the differences among nodes and edges are the key issues to describe the network properties and their dynamical social processes [3].
On the contrary, the complex systems tradition deals with large but poor networks. Making the assumption of statistical equivalence of graph entities, a mean field treatment is put in place to describe the aggregated properties of the network [4].
The actual network datasets contain an unprecedented quantity of relational information at all, and between all, the possible levels: individuals, social groups, political structures, economical actors, etc. We finally deal with large and rich network structures and we can realize the implicit limits of the two mentioned above approaches [5]: the traditional methods from social science cannot be upscaled because of their algorithmic complexity and those from complex systems lose track of the complex nature of the actors, their relationships and their processes.
Our workshop has the aim of developing an interdisciplinary reflection on how methods from social science could be up-scaled to large network structures and on how methods from complex systems could be down-scaled to deal with small heterogeneous structures [6]. This will be the starting point of a larger discussion on an interdisciplinary network science including new analytical and modelling tools able to face the challenge of large and rich network structures: statistical and visualization treatments able to deal with the relevant heterogeneity of the nodes, tools for the identification of the relevant dimensions on which a mean field treatment could make sense, algorithms to include in the network analysis the structure and the dynamics of the intermediate level aggregates (social groups, etc.). Methodological and case study approaches will be both present in the workshop.
[1] Lazer, David, et al. "Life in the network: the coming age of computational social science." Science (New York, NY)323.5915 (2009): 721.
[2] Conte, Rosaria, et al. "Manifesto of computational social science." European Physical JournalSpecial Topics 214 (2012): p-325.
[3] Scott, John. Social network analysis. Sage, 2017.
[4] Barrat, Alain, Marc Barthelemy, and Alessandro Vespignani. Dynamical processes on complex networks. Cambridge university press, 2008.
[5] Lazega, Emmanuel, and Christophe Prieur. "Sociologie néostructurale, disciplines sociales et systèmes complexes." Revue Sciences/Lettres 2 (2014).
[6] Breiger, Ronald L. (2015). "Scaling down". Big Data & Society, 2(2), 2053951715602497.
