Matteo Bruno PhD

Matteo Bruno PhD

Sony Computer Science Laboratories Paris

I have always been fascinated by how simple fundamental laws of nature can generate the complexity of our world. Complexity science is one of the modern tools of science to understand biological and social interactions. Simple structures mix and combine to give rise to higher order objects, and many parts of this long process are still unknown and hard to discover. In my research, I want to explore different topics to discover why seemingly unrelated systems actually work in similar ways. In particular, I am interested in the causes of human behaviour and how from biological and social needs of living creatures their complex interactions are born and evolve. I believe understanding these drivers can help create a better and more harmonious world. After finishing my PhD in network science, I joined Sony CSL in Rome to work in the creativity team, and in particular on topics of urban mobility. Here we work on new exciting projects at the forefront of science, with a focus on research that can be useful to build a better future and society. Every day there is the opportunity for me to learn and discover something new.

Bipartite networks are everywhere, and so are bots: a case study on the Twitter discussion on Brexit

Every complex system that involves two different kinds of agents interacting with each other can be described as a bipartite network, whose analysis can give new information about the system itself. Examples of these kind of systems can be found in many different areas of science, such as ecology, economics, social science. However, every graph can be remapped to a bipartite nodes-links graph. As this kind of framework gains more and more attention, we take a look at the bipartite null models and projection methods that exist, and how to deal with networks of large size and density. We focus our attention on a case study of the Twitter debate on Brexit during the UK elections of 2019, where we are able to build several (bipartite) networks of interactions between users and to characterize the presence and activity of automated accounts. Among the results of this study, we find that malicious users are injected in the debate at crucial times, that there is a class of suspicious users which are able not to be suspended by Twitter by maintaining a low profile, and that there are bots polluting the Brexit discourse with other populist topics. Useful references: Bruno et al., Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election, 2022 Vallarano et al., Fast and scalable likelihood maximization for Exponential Random Graph Models with local constraints, 2021 Saracco et al., Inferring monopartite projections of bipartite networks: an entropy-based approach, 2017 Python package: