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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Nestedness in complex networks: Observation, emergence, and implications. Physics Reports, 813:1-90.
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Temporal similarity metrics for latent network reconstruction: The role of time-lag decay. Information Sciences, 489:182-192.
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The long-term impact of ranking algorithms in growing networks. Information Sciences, 488:257-271.
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Fast influencers in complex networks. Communications in Nonlinear Science and Numerical Simulation, 74:69-83.
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Influencers identification in complex networks through reaction-diffusion dynamics. Physical review. E, 98:062302.
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Link Prediction in Bipartite Nested Networks. Entropy, 20(10):777.
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Revealing in-block nestedness: Detection and benchmarking. Physical review. E, 97(6):062302.
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Randomizing growing networks with a time-respecting null model. Physical review. E, 97(5):052311.
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Early identification of important patents through network centrality. INET Oxford Working Papers 2017-12, University of Zurich.