Computational social science – Data Big and Small

As part of the upcoming NetSci2018 conference in Paris, I co-organize a satellite event that aims to foster interdisciplinary reflection on how methods from social science can be upscaled to large network structures and on how methods from complex systems can be downscaled to deal with small heterogeneous structures.

The idea is to reconcile two traditions of research that have remained separate so far. Sociology typically handles small but rich networks where a wealth of network attributes results from the complexity of the data collection design. Differences across nodes and edges enable to capture the social processes underlying network structures and their dynamics. Instead, the complex systems tradition handles large but poorly-specified networks. Assuming statistical equivalence of graph entities, a mean field treatment suffices to describe the aggregate properties of the network. Today’s network data-sets contain an unprecedented quantity of relational information within and between all possible levels: individuals, social groups, organizations, and macro entities. Such large and rich network structures expose the implicit limitations of the two above-mentioned approaches: classical sociological methods cannot be upscaled because of their heavy algorithms, and those from complex systems lose track of the multi-faceted nature of social actors, their relationships and their processes.

Our satellite event aims to move forwards, inviting an inter-disciplinary reflection and exploring ways in which these limitations can be overcome.

The program of the satellite is now online.

I am most pleased to co-organize this satellite event with Floriana Gargiulo, Sylvie Huet, and Emmanuel Lazega.

We are honored to count, among our invited speakers, Camille Roth, Matthieu Latapy, Fariba Karimi, and Noshir Contractor.