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We bridge the properties of the regular square and honeycomb Voronoi tessellations of the plane to those of the Poisson-Voronoi case, thus analyzing in a common framework symmetry-break processes and the approach to uniformly random…

Statistical Mechanics · Physics 2011-10-11 Valerio Lucarini

We suggest an approach to study hierarchy, especially hidden one, of complex networks based on the analysis of their vulnerability. Two quantities are proposed as a measure of network hierarchy. The first one is the system vulnerability V.…

Disordered Systems and Neural Networks · Physics 2007-05-23 V. Gol'dshtein , G. A. Koganov , G. I. Surdutovich

We introduce a new class of networks that grow by enhanced redirection. Nodes are introduced sequentially, and each either attaches to a randomly chosen target node with probability 1-r or to the ancestor of the target with probability r,…

Statistical Mechanics · Physics 2013-11-14 Alan Gabel , P. L. Krapivsky , S. Redner

The self averaging properties of conductance $g$ are explored in random resistor networks with a broad distribution of bond strengths $P(g)\simg^{\mu-1}$. Distributions of equivalent conductances are estimated numerically on hierarchical…

Condensed Matter · Physics 2009-10-22 R. F. Angulo , E. Medina

Voronoi mosaics inspired by the seed points placed on the Archimedes Spirals are reported. Voronoi entropy was calculated for these patterns. Equidistant and non-equidistant patterns are treated. Voronoi mosaics built from cells of equal…

History and Overview · Mathematics 2020-07-01 Mark Frenkel , Irina Legchenkova , Edward Bormashenko

Turing instability in activator-inhibitor systems provides a paradigm of nonequilibrium pattern formation; it has been extensively investigated for biological and chemical processes. Turing pattern formation should furthermore be possible…

Adaptation and Self-Organizing Systems · Physics 2010-05-13 Hiroya Nakao , Alexander S. Mikhailov

We study the richness of the ensemble of graphical structures (i.e., unlabeled graphs) of the one-dimensional random geometric graph model defined by $n$ nodes randomly scattered in $[0,1]$ that connect if they are within the connection…

Information Theory · Computer Science 2022-06-24 Mihai-Alin Badiu , Justin P. Coon

Disordered hyperuniform (DHU) materials are an emerging class of exotic heterogeneous material systems characterized by a unique combination of disordered local structures and a hidden long-range order, which endow them with unusual…

Materials Science · Physics 2025-04-11 Liyu Zhong , Sheng Mao , Yang Jiao

We consider networks with two types of nodes. The v-nodes, called centers, are hyper- connected and interact one to another via many u-nodes, called satellites. This central- ized architecture, widespread in gene networks, possesses two…

Molecular Networks · Quantitative Biology 2012-03-02 S. A. Vakulenko , O. Radulescu

Over the past several years, numerous authors have explored model theoretically motivated combinatorial conditions that ensure that a graph has an efficient regular decomposition in the sense of Szemer\'edi. In this paper we set out a…

Combinatorics · Mathematics 2025-03-18 C. Terry , J. Wolf

We study criteria for and properties of boundary-to-boundary holography in a class of spin network states defined by analogy to projected entangled pair states (PEPS). In particular, we consider superpositions of states corresponding to…

Quantum Physics · Physics 2022-05-23 Simon Langenscheidt

We study the effects of nonreciprocity and network structure on percolation. To this end, we investigate nonreciprocal random networks - directed networks for which the probability of a link occurring from node i to node j differs from the…

Statistical Mechanics · Physics 2025-10-07 Chanania Steinbock

We describe fluctuations in finite-size networks with a complex distribution of connections, $P(k)$. We show that the spectrum of fluctuations of the number of vertices with a given degree is Poissonian. These mesoscopic fluctuations are…

Statistical Mechanics · Physics 2009-11-07 S. N. Dorogovtsev , A. N. Samukhin

Delaunay triangulation can be considered as a type of complex networks. For complex networks, the degree distribution is one of the most important inherent characteristics. In this paper, we first consider the two- and three-dimensional…

Physics and Society · Physics 2018-05-22 Gang Mei , Nengxiong Xu , Salvatore Cuomo

Hyperuniform systems are distinguished by an unusually strong suppression of large-scale density fluctuations and, consequently, display a high degree of uniformity at the largest length scales. In some cases, however, enhanced uniformity…

Statistical Mechanics · Physics 2025-10-24 Carlo Vanoni , Paul J. Steinhardt , Salvatore Torquato

We introduce the concept of a hyperuniformity disorder length that controls the variance of volume fraction fluctuations for randomly placed windows of fixed size. In particular, fluctuations are determined by the average number of…

Soft Condensed Matter · Physics 2017-09-18 A. T. Chieco , R. Dreyfus , D. J. Durian

Disordered hyperuniform dispersions are exotic amorphous two-phase materials characterized by an anomalous suppression of long-wavelength volume-fraction fluctuations, endowing them with novel physical properties. While such unusual…

Soft Condensed Matter · Physics 2019-03-12 Jaeuk Kim , Salvatore Torquato

Various approaches and measures from network analysis have been applied to granular and particulate networks to gain insights into their structural, transport, failure-propagation and other systems-level properties. In this article, we…

Soft Condensed Matter · Physics 2019-11-06 Silvia Nauer , Lucas Böttcher , Mason A. Porter

Percolation processes on random networks have been the subject of intense research activity over the last decades: the overall phenomenology of standard percolation on uncorrelated and unclustered topologies is well known. Still some…

Statistical Mechanics · Physics 2024-12-06 Lorenzo Cirigliano , Gábor Timár , Claudio Castellano

We propose a network structure discovery model for continuous observations that generalizes linear causal models by incorporating a Gaussian process (GP) prior on a network-independent component, and random sparsity and weight matrices as…

Machine Learning · Computer Science 2017-03-01 Amir Dezfouli , Edwin V. Bonilla , Richard Nock
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