Dr. Manuel Sebastian Mariani
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Dr. Manuel Mariani Senior Research Associate |
Short biography
Manuel's main research mission is to understand how, in modern social and economic systems, individuals' heterogeneous behavior and algorithms shape the emergence of collective social phenomena. This involves, for example, understanding how to individual-level choices lead to the collective success (or failure) of individuals, products, and businesses; how to design effective influencer marketing strategies that take into account individuals' behavioral patterns; how individual-level heuristics to form opinions on interconnected topics may lead to fragile collective opinions. From a broader perspective, Manuel views this understanding as a stepping stone to the design of better-performing and more sustainable social environments. To progress in his mission, Manuel aims to creatively develop and combine tools based on networks science, agent-based modeling, machine learning, and experiments. His research has been published in both interdisciplinary journals and disciplinary ones in physics, computer science, and innovation management [see: https://scholar.google.com/citations?user=AEBRJZcAAAAJ&hl=en]. When Manuel is not editing long TEX files, you'll likely find him running in Zurich's hills or swimming in the lake. |
Research interests
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Publications
ZORA Publication List
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Publications
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Collective dynamics behind success. Nature Communications, 15(1):10701.
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The quest for an unbiased scientific impact indicator remains open. Proceedings of the National Academy of Sciences of the United States of America, 121(41):e24100211.
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Forecasting national CO2 emissions worldwide. Scientific Reports, 14:22438.
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Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media. National Science Review, 11(7):nwae073.
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Integrating Behavioral Experimental Findings into Dynamical Models to Inform Social Change Interventions. SSRN 4837583, University of Zurich.
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Citations or dollars? Early signals of a firm’s research success. Technological Forecasting and Social Change, 201:123208.
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Ranking species in complex ecosystems through nestedness maximization. Communications Physics, 7(102):online.
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Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms. Journal of Physics: Complexity, 5(1):015010.
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The different structure of economic ecosystems at the scales of companies and countries. Journal of Physics: Complexity, 4(2):025011.
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Towards detecting previously undiscovered interaction types in networked systems. IEEE Internet of Things Journal, 9(20):20422-20430.
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The simple regularities in the dynamics of online news impact. Journal of Computational Social Science, 5(1):629-646.
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Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks. Communications in Nonlinear Science and Numerical Simulation, 104:106029.
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Forecasting countries' gross domestic product from patent data. Chaos, Solitons & Fractals, 160:112234.
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The local structure of citation networks uncovers expert-selected milestone papers. Journal of Informetrics, 15(4):101220.
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Absence of a resolution limit in in-block nestedness. Communications in Nonlinear Science and Numerical Simulation, 94:105545.
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Network-based ranking in social systems: three challenges. Journal of Physics: Complexity, 1(1):011001.
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Recommending investors for new startups by integrating network diffusion and investors’ domain preference. Information Sciences, 515:103-115.
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Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data. Journal of Informetrics, 14(1):101005.
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Optimal timescale for community detection in growing networks. New Journal of Physics, 21(9):093066.
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Early identification of important patents: Design and validation of citation network metrics. Technological Forecasting and Social Change, 146:644-654.