Modelling dynamical processes in complex socio-technical systems

By Alessandro Vespignani. Nature Physics, 2011

643 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b7ec3880b9761f140cc922d3fe9340c6d3646511

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Citations in the corpus

Human mobility: Models and applications (2017, background): “Indeed, the elucidation and understanding of these patterns was motivated by its relation to several real-world applications such as traffic forecasting [3, 13, 14], urban planning [15, 10], internal security [16, 17] and epidemic modeling [18, 19, 20] to name but a few.”
Languages cool as they expand: Allometric scaling and the decreasing need for new words (2012, background): “[4] Vespignani, A.”
Multilayer Analysis and Visualization of Networks (2014, background): “Such efforts have yielded numerous insights in many areas of science and technology [2–16].”
Activity driven modeling of time varying networks (2012, result): “ed network, characterized by a topology dened just on average because the connectivity patterns has a dynamic extremely fast with respect to the dynamical process, heterogeneous mean-eld approaches [2, 6] predict an epidemic threshold that is inversely proportional to the second moment of the networ”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “In fact, the currently growing available human generated data and computing power have driven the fast emergence of various social, technological and biological networks [145] [146] [147] [148] .”
Memory in network flows and its effects on spreading dynamics and community detection (2013, methodology): “data; (3) we report how we performed the signiﬁcance analysis with bootstrapping; (4) we describe the ranking and community detection algorithms we developed for analyzing memory effects in the data; (5) we explain how we generalize commonly used compartmental models for spreading dynamics to includ”
The Matthew effect in empirical data (2014, background): “Recent leaps of progress in the availability of reliable “big data”, mathematical modelling and informatics tools enable increasingly deeper understanding of contagion processes, emerging tipping points, cascading and related nonlinear phenomena that underpin the most interesting characteristics of ”
Reputation and impact in academic careers (2013, background): “Indeed, it is likely that agent-based reputation mechanisms will play an increasing role in society due to the omnipresence of online recommender systems governed by reputation dynamics operating as a general diffusive contagion phenomena (36).”