Dynamical Processes on Complex Networks

By Alain Barrat, Marc Barthlemy, Alessandro Vespignani. 2008

2,449 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bf7b349fc87dfa91fc85f7363b594b9615371717

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In Statistical and Nonlinear Physics

Citations in the corpus

Complex networks: The fragility of interdependency (2010, neutral): “In this context, a large body of research has shown that most real-world infrastructure networks present globally dynamic self-organization and a high level of hetero geneity characterized by statistical distributions that vary over several orders of magnitude 6,7 .”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “The study of opinion dynamics and information diffusion in social networks has a long history in the social, physical, and computational sciences [38, 17, 5, 13, 6, 31, 32].”
Dynamics on higher-order networks: a review (2022, background): “Indeed, network science has proven highly efficient in elucidating the dynamics of complex systems arising from many different contexts in the physical, biological as well as technological and social sciences [3,4].”
Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network (2013, methodology): “Economics also made use of the concepts of network science, gaining additional insight to the more traditional approach [8–13].”
Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number (2011, methodology): “In our network [20,21] we introduce the weight wij of each edge, defined as the number of times user i replies to user j as a direct measurement of the interaction strength between two users and stable relations will be those with a large weight.”
Predicting the Behavior of Techno-Social Systems (2009, background): “…technology and the dissolution of boundaries between the cyberworld and our real-world social activities are changing ouraccessibilitytodata,leadingto “ reality-mining, ” which has been defined as the collection of machine-sensed environmental data that are re-lated to human social behavior ( 2 ).”
Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering (2013, background): “With great theoretical and practical significance, the studies on epidemic and information spreading in biological, social and technological networks become one of the most exciting domains in many branches of sciences [1–4].”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “This result is in line with classic results from epidemic spreading on heterogenous networks which show that targeted measures aimed at isolating few central individuals are much more efficient than average measures across the whole system [142, 143].”
Multilayer networks (2013, background): “However, diseasespreading and information-spreading processes typically take place on networks, so it is important to study the effects of network structure on such processes [22,245].”