Centralities in complex networks

التفاصيل البيبلوغرافية
العنوان: Centralities in complex networks
المؤلفون: Bovet, Alexandre, Makse, Hernán A.
المصدر: In: Meyers R.A. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg (2021)
سنة النشر: 2021
المجموعة: Computer Science
Physics (Other)
مصطلحات موضوعية: Physics - Physics and Society, Computer Science - Social and Information Networks
الوصف: In network science complex systems are represented as a mathematical graphs consisting of a set of nodes representing the components and a set of edges representing their interactions. The framework of networks has led to significant advances in the understanding of the structure, formation and function of complex systems. Social and biological processes such as the dynamics of epidemics, the diffusion of information in social media, the interactions between species in ecosystems or the communication between neurons in our brains are all actively studied using dynamical models on complex networks. In all of these systems, the patterns of connections at the individual level play a fundamental role on the global dynamics and finding the most important nodes allows one to better understand and predict their behaviors. An important research effort in network science has therefore been dedicated to the development of methods allowing to find the most important nodes in networks. In this short entry, we describe network centrality measures based on the notions of network traversal they rely on. This entry aims at being an introduction to this extremely vast topic, with many contributions from several fields, and is by no means an exhaustive review of all the literature about network centralities.
Comment: 10 pages, 3 figures. Entry for the volume "Statistical and Nonlinear Physics" of the Encyclopedia of Complexity and Systems Science, Chakraborty, Bulbul (Ed.), Springer, 2021 Updated version
نوع الوثيقة: Working Paper
DOI: 10.1007/978-3-642-27737-5_765-1
URL الوصول: http://arxiv.org/abs/2105.01931
رقم الأكسشن: edsarx.2105.01931
قاعدة البيانات: arXiv
الوصف
DOI:10.1007/978-3-642-27737-5_765-1