Supervisor: Prof Vito Latora
Project description:
Science and technology play a crucial role in our modern society. They are the driving forces towards innovation, societal transformations, and economic growth. The processes through which scientists, inventors and entrepreneurs explore the world in search of new ideas and opportunities, produce and diffuse innovation are intimately related. The vast amount of available data on scientific collaborations and scientific products on the one hand, and on small and medium enterprises, such as startups, and their successful products on the other hand, offers today a unique opportunity to investigate in a quantitative and systematic way creativity and success in large-scale innovation ecosystems.
The purpose of this PhD project is to integrate information from multiple data sources (such as Google Scholar, Web of Science, Cruchbase.com and Angelist.co) to map over time and across the entire world the interaction networks of scientists working in different fields, entrepreneurs and startups from different market sectors. We all know that creativity and success depend both on talent and experience, but also on communication, networking and access to knowledge andinformation. We will then measure and model quantitatively the extent to which the network of informal and formal relationships between people and institutions influences creativity, affects the productivity and the performance of scientists and inventors, the success of a start-up, finally determining the rise or fall of a scientific field or of an innovation ecosystem. The overall goal of this project is the combined use of digital data and mathematical modelling to understand how we explore the world in search of novelties, and to unveil why and how certain novel ideas and products become viral, while others have no significant impact whatsoever. Based on these results we will develop algorithms to predict the success of novel scientific topics and business ideas, and we will design methodologies to guide and support innovation process systematically, with the hope to reduce the intrinsic risks associated to innovation.
The perfect candidate will hold an MSc or an equivalent degree in applied mathematics, physics or engineering, and will have a good background in complex systems, network science, and experience with computer programming and numerical simulations.
REFERENCES- Network dynamics of innovation processesI Iacopini, S Milojevic, V Latora, Phys. Rev. Lett. 120, 048301 (2018)- Interacting discovery processes on complex networksI Iacopini, G Di Bona, E Ubaldi, V Loreto, V Latora, Phys. Rev. Lett. 125, 2 48301 (2020)- The evolution of knowledge within and across fields in modern physicsY Sun, V Latora, Scientific Reports 10, 12097 (2020)- Interdisciplinary researchers attain better performance in fundingY Sun, G Livan, A Ma, V Latora, Commun Phys 4, 263 (2021)- Predicting success in the worldwide start-up networkM Bonaventura, V Ciotti, P Panzarasa, S Liverani, L Lacasa, V Latora, Scientific Reports 10, 345 (2020)- Quantifying and predicting success in show businessOE Williams, L. Lacasa, V. Latora, Nature Communications 10, 2256 (2019)- Anatomy of funded research in science,A. Ma, R.J. Mondragon, V. Latora, PNAS 112, 14760, 2015- Predicting urban innovation from the US Workforce Mobility NetworkM Bonaventura, LM Aiello, D Quercia, V Latora, Humanities and Social Sciences Comm. 8, 10 (2021)
Further information:
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Entry requirements
Fees and funding
Supervisor: Professor Vito Latora
Our world is highly interconnected and dynamical. Social interactions and human mobility affect the way in which ideas, but also transmitted diseases, propagate and can become widespread. The interconnectedness of financial institutions affects instability and credit crises. Analogously, a single failure can lead to a complete collapse of a power grid through a cascading failure mechanism. We therefore live on this delicate balance in which we would like to have, on the one hand, more social and communication links to better propagate innovation, good social habits and novel technological products but, on the other hand, fewer links to limit the propagation of biological diseases, or to contain cascading failures in critical infrastructures and systemic risk in financial systems.
The purpose of this PhD project is to develop a general framework to analyse and model spreading processes in complex networks. For this we will:1) distinguish between simple contagion and complex contagion mechanisms, and propose novel models of contagion processes, biological and social epidemics, and cascading failures. We will use complex networks but also higher-order networks, such as hypergraphs simplicial complexes, to model interactions in groups of size larger than two.2) find initial seeds and topological patters that favour spreading, but also devise counteracting strategies consisting in minimal changes of the network structure that can contain epidemics or completely inhibit cascading failures.3) consider practical applications to socio-economic systems and to energy systems. E.g., in power grids, limiting connectivity for the sake of additional security (avoiding cascading failures) is not always desirable, and highly connected structures (microgrids) are heavily discussed while the overall demand for electric power transmission increases.
The perfect candidate will hold an MSc in applied mathematics, physics or engineering, and will have a good background in network science, and experience with mathematical modelling.
REFERENCES- Simplicial models of social contagion,I Iacopini, G Petri, A Barrat, V Latora, Nature Communications 10, 2485 (2019)- The physics of higher-order interactions in complex systems,F Battiston et al., Nat. Phys. 17, 1093 (2021)- Stability of synchronization in simplicial complexesLV Gambuzza et al., Nature Communications 12, 1255 (2021)- Inhibiting failure spreading in complex networksF Kaiser, V Latora, D Witthaut, Nature Communications 12, 3143 (2021)- Dynamically induced cascading failures in power gridsB. Schafer, D. Witthaut, M. Timme, V. Latora, Nature Communications 9, 1975 (2018)- Multi-layer modelling of adoption dynamics in energy demand managementI Iacopini, B Schafer, E Arcaute, C Beck, V Latora, Chaos 30, 013153 (2020)- A dynamic approach merging network theory and credit risk to assess systemic risk in financial networksD Petrone, V Latora, Scientific Reports 8, 5561 (2018)- Lack of practical identifiability may hamper reliable predictions in epidemic modelsL Gallo, M Frasca, V Latora, G Russo, Science Advances 8(3):eabg5234 (2022)- On the dual nature of adoption processes in complex networksI Iacopini, V Latora, Front. Phys. 9, 604102 (2021)