Mapping Scientific Collaboration Through Complex Networks: A Case Study on EDIESCA

In a highly networked world, access to information is becoming easier and more efficient. For the scientific community, however, this is not enough to guarantee the development of collaborations or the emergence of research groups. Mexico hosts the Meeting for the Dissemination and Research in the Study of Complex Systems and their Applications (EDIESCA, by its acronym in Spanish), a young conference that is steadily gaining participants among scientists working on dynamical and complex systems. In this work, we describe the evolution of co-authorship among EDIESCA participants from its inception in 2019 to 2025 from the perspective of complex networks. Using participation and co-authorship data collected across the six editions of the event, we identify the emergence and evolution of collaborative groups, defined as the connected components of each edition’s network, and characterize their structural properties and author productivity, measured as the average number of works per author in each edition. In addition, degree, harmonic, and betweenness centrality are used to identify authors with a prominent structural position within each network. The networks are sparse and fragmented; their largest connected components combine high local clustering with average path lengths that are comparable to, or moderately longer than, those of random reference graphs. These results provide a descriptive account of the co-authorship patterns recorded at a specialized academic event; since co-authorship may predate participation, the data do not allow us to attribute the observed collaborations to the event itself.