Department Systems Analysis, Integrated Assessment and Modelling


Photo by Francesco Ungaro from Pexels

Community detection consists of extracting the affinity between agents of a system, which
is extracted from quantities such as the frequency of interactions. When analyzing datasets,
however, the absence of a connection between agents might not be due to a lack of affinity,
but rather to the fact that these agents never met. For example, Sandra and Paul would like
each other a lot, if they only met.
We introduce exposure into community detection, as an additional mechanism to explain the
lack of links among agents. For problems in which being exposed to other agents is crucial
towards the development of an affinity, this leads to enhanced community detection. In our
example, our community detection scheme is aware that if Sandra and Paul never met, it
might be either because they are incompatible, or just because they have not met yet.


Othman, S.; Schulz, J.; Baity-Jesi, M.; De Bacco, C. (2023) Modeling node exposure for community detection in networks, In: Cherifi, H.; Mantegna, R. N.; Rocha, L. M.; Cherifi, C.; Micciche, S. (Eds.), Complex networks and their applications XI. Proceedings of the eleventh international conference on complex networks and their application, doi:10.1007/978-3-031-21131-7_18, Institutional Repository


Dr. Marco Baity Jesi Group Leader (he/him) Tel. +41 58 765 5793 Send Mail