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ABSTRACT / Collaboration networks of scientists are a prime example of complex social systems. We study co-authorship networks to quantify the impact of social constituents, e.g. of previous co-authors, joint publications, on the success of publications as measured by their number of citations. This requires to solve different problems which are addressed in the talk: (i) to model growing networks with two coupled layers, the network of authors and the network of publications, (ii) to generate and test different hypotheses about the coupling between these two layers, (iii) to estimate parameters and compare models with different complexity. But it is worth the effort: After all, producing academic publications is a social endeavour, and our results shed more light on social feedback mechanisms and successful career paths of authors.
[1] V. Nanumyan, C. Gote, F. Schweitzer: Multilayer network approach to modeling authorship influence on citation dynamics in physics journals, Physical Review E 102, 032303 (2020), doi: 10.1103/PhysRevE.102.032303
[2] C. Zingg, V. Nanumyan, F. Schweitzer: Citations Driven by Social Connections? A Multi-Layer Representation of Coauthorship Networks, Quantitative Science Studies 1, 1493-1509 (2020), doi: 10.1162/qss_a_00092
[3] M.V. Tomasello, G. Vaccario, F. Schweitzer: Data-driven modeling of collaboration networks: A cross-domain analysis, EPJ Data Science 6, 22 (2017), doi: 10.1140/epjds/s13688-017-0117-5
[4] E. Sarigol, R. Pfitzner, I. Scholtes, A. Garas, F. Schweitzer: Predicting Scientific Success Based on Coauthorship Networks, EPJ Data Science 3, 9 (2014), doi: 10.1140/epjds/s13688-014-0009-x
BIO / Frank Schweitzer has been Full Professor for Systems Design at ETH Zurich and also associated member of the Department of Physics at the ETH Zurich. His focuses on applications of complex systems theory in the dynamics of social and economic organizations. He is interested in phenomena as diverse as user interaction in online social networks, collective decisions in animal groups, failure cascades and systemic risk in economic networks, and the rise and fall of collaborations in socio-technical systems. His methodological approach can be best described as data-driven modeling, i.e., it combines the insights from big data analysis with the power of agent-based computer simulations and the strength of rigorous mathematical models.