Scale-Free Networks - Complex Webs in Nature and Technology

By Guido Caldarelli. 2013

296 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f48218d768ae32ba5caa762dedcdfac80f9b37b7

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Citations in the corpus

Analysing ecological networks of species interactions (2018, background): “It is often assumed that the distribution of degree in networks should resemble a power law (Strogatz, 2001; Caldarelli, 2007).”
Network control principles predict neuron function in the Caenorhabditis elegans connectome (2017, background): “The nonlinearity of system (1) must be considered if we want to find out how to control the muscles.”
The fundamental advantages of temporal networks (2016, background): “For example, within cellular metabolic networks [1], the links correspond to brief chemical reactions; in social networks [2–7] friendship links are inferred from face-to-face or digital communications of short duration.”
Vital nodes identification in complex networks (2016, background): “The scale-free property [5, 11, 12] indicates that the roles of different nodes in the structure and function of a network may be largely different.”
Universality in network dynamics (2013, unknown): “Although these differences are clearly encoded somehow in the functional form of W (xi) and Q(xi,xj) in equation (1), at present we have no way of predicting how a system responds to perturbations from the analytical formulation of the underlying dynamics.”
Target control of complex networks (2014, unknown): “We observe a peak of target control efﬁciency E for SF networks with degree exponent g A [2, 3], which is the range of g relevant for many real-world networks 37,38 (Fig.”
Human mobility, social ties, and link prediction (2011, background): “Human Mobility, Social Ties, and Link Prediction Dashun Wang1,2 Dino Pedreschi1,3 Chaoming Song1,2 Fosca Giannotti1,4 Albert-László Barabási1,2,5 1CCNR, Dept. of Physics and Computer Science, Northeastern University, Boston, MA 02115, USA 2CCSB, Dana-Farber Cancer Institute, Harvard University,…”
Hierarchical Information Clustering by Means of Topologically Embedded Graphs (2011, unknown): “Indeed, networks naturally reflect in their set of vertices the variety of elements in the system, they reflect in their edges the plurality of the interrelations between elements and they encode in their dynamics the complex evolution and adaptation of the system [12–16].”