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We introduce two sandpile models which show the same behavior of real sandpiles, that is, an almost self-organized critical behavior for small systems and the dominance of large avalanches as the system size increases. The systems become…

Statistical Mechanics · Physics 2009-11-07 Maria de Sousa Vieira

Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal…

Physics and Society · Physics 2015-02-11 Jin-Long Liu , Zu-Guo Yu , Vo Anh

The Abelian sandpile model was the first example of a self-organized critical system studied by Bak, Tang and Wiesenfeld. The dynamics of the sandpiles occur when the grains topple over a graph. In this study, we allow the graph to evolve…

Combinatorics · Mathematics 2024-07-24 Carlos A. Alfaro , Juan Pablo Serrano , Ralihe R. Villagrán

The coupling of scale-free networks with mobile unstructured networks is certainly unusual. In mobile networks, connections active at a given instant are constrained by the geographical distribution of mobile nodes, and by the limited…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-11-17 Stefano Ferretti , Vittorio Ghini

In the sandpile model, vertices of a graph are allocated grains of sand. At each unit of time, a grain is added to a randomly chosen vertex. If that causes its number of grains to exceed its degree, that vertex is called unstable, and…

Combinatorics · Mathematics 2024-09-19 Thomas Selig , Haoyue Zhu

Consider designing a transportation network on $n$ vertices in the plane, with traffic demand uniform over all source-destination pairs. Suppose the cost of a link of length $\ell$ and capacity $c$ scales as $\ell c^\beta$ for fixed…

Disordered Systems and Neural Networks · Physics 2008-03-17 David J. Aldous

Recently, one paper in Nature(Papadopoulos, 2012) raised an old debate on the origin of the scale-free property of complex networks, which focuses on whether the scale-free property origins from the optimization or not. Because the…

Social and Information Networks · Computer Science 2012-12-04 Bojin Zheng , Hongrun Wu , Jun Qin , Wenfei Lan , Wenhua Du

We study the detailed mechanism of the failure of scale-free networks under intentional attacks. Although it is generally accepted that such networks are very sensitive to targeted attacks, we show that for a particular type of structure…

Physics and Society · Physics 2009-11-13 Lazaros K. Gallos , Panos Argyrakis

We investigate the higher-order connectivity of scale-free networks using algebraic topology. We model scale-free networks as preferential attachment graphs, and we study the algebraic-topological properties of their clique complexes. We…

Algebraic Topology · Mathematics 2025-02-25 Chunyin Siu

We investigate the optimal design of networks for a general transport system. Our network is built from a regular two-dimensional ($d=2$) square lattice to be improved by adding long-range connections (shortcuts) with probability $P_{ij}…

Physics and Society · Physics 2014-04-24 G. Li , S. D. S. Reis , A. A. Moreira , S. Havlin , H. E. Stanley , J. S. Andrade

Yes and no. The size of the largest neighbourhood in a Barabasi-Albert scale-free entwork has string fluctuations of the order of the average value. The number of sites having exactly ten neighbours increases linearly in the network size…

Disordered Systems and Neural Networks · Physics 2007-05-23 Dietrich Stauffer , Amnon Aharony

We study the synchronization transition in scale-free networks that display power-law asymptotic behaviors in their degree distributions. The critical coupling strength and the order-parameter critical exponent derived by the mean field…

Statistical Mechanics · Physics 2007-05-23 Deok-Sun Lee

We generalize the scale-free network model of Barab\`asi and Albert [Science 286, 509 (1999)] by proposing a class of stochastic models for scale-free interdependent networks in which interdependent nodes are not randomly connected but…

Physics and Society · Physics 2015-06-11 Boris Podobnik , Davor Horvatic , Mark Dickison , H. Eugene Stanley

We numerically study bootstrap percolation on Kleinberg's spatial networks, in which the probability density function of a node to have a long-range link at distance $r$ scales as $P(r)\sim r^{\alpha}$. Setting the ratio of the size of the…

Physics and Society · Physics 2014-08-07 Jian Gao , Tao Zhou , Yanqing Hu

BDeu marginal likelihood score is a popular model selection criterion for selecting a Bayesian network structure based on sample data. This non-informative scoring criterion assigns same score for network structures that encode same…

Machine Learning · Computer Science 2012-06-26 Tomi Silander , Petri Kontkanen , Petri Myllymaki

Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional distributions through pseudo-Gibbs sampling. Despite their…

Machine Learning · Computer Science 2026-04-02 Kazuya Takabatake , Shotaro Akaho

In many situations we are interested in the propagation of energy in some portions of a three dimensional system with dilute long-range links. In this paper sandpile model is defined on the three-dimensional small world network with real…

Statistical Mechanics · Physics 2018-03-21 M. N. Najafi , H. Dashti-Naserabadi

A random network is grown by introducing at unit rate randomly selected nodes on the Euclidean space. A node is randomly connected to its $i$-th predecessor of degree $k_i$ with a directed link of length $\ell$ using a probability…

Statistical Mechanics · Physics 2009-11-07 S. S. Manna , Parongama Sen

The Bak-Tang-Wiesenfeld sandpile model provdes a simple and elegant system with which to demonstate self-organized criticality. This model has rather remarkable mathematical properties first elucidated by Dhar. I demonstrate some of these…

Statistical Mechanics · Physics 2009-11-10 Michael Creutz

In this paper we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the number of observations are fewer than the number of…

Machine Learning · Computer Science 2022-08-25 Mahdi Soltanolkotabi , Adel Javanmard , Jason D. Lee