I’m happy and honoured to speak today at the “Pathways in Network Science” online seminar of the Women in Network Science (WiNS) group.
Pathways in Network Science aims to give the stage to women and nonbinary researchers in network science to share their career paths or some pertinent aspects of it. Presentations can be a summary of the research topics explored along a speaker’s career path or even an autobiographical presentation about how opportunities and challenges influenced her aspirations and impacted her career path. It can also include discussing gender-related challenges and experience with individual strategies and/or systemic changes.
I’ll talk about myself in terms of mobility – both geographic and disciplinary – and the challenges and opportunities it represented. I’ll also talk about resilience – or how network science helped me to make true my dream of devoting my life to research. I’ll mention impact – or how to think of the place of science in society, and how network research can lead to positive change. I’ll conclude with the challenges ahead – and how they are not only scientific, but also deeply human and social.
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.