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This Letter introduces a generalization of known duplication-divergence models for growing random graphs. This general duplication-divergence model includes a new coupled divergence asymmetry rate, which allows to obtain the structure of…

Statistical Mechanics · Physics 2024-12-04 Dario Borrelli

In this paper we consider the clustering coefficient and clustering function in a random graph model proposed by Krioukov et al.~in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are…

Probability · Mathematics 2020-12-18 Nikolaos Fountoulakis , Pim van der Hoorn , Tobias Müller , Markus Schepers

In this note we study the geometry of the largest component C_1 of critical percolation on a finite graph G which satisfies the finite triangle condition, defined by Borgs et al. There it is shown that this component is of size n^{2/3}, and…

Probability · Mathematics 2009-11-17 Gady Kozma , Asaf Nachmias

In 2002, Chung and Lu introduced a version of the Erdos-Renyi model which an edge between $i$ and $j$ is present with probability $p(i,j)$. They applied this model to compute the diameter of power-law random graphs, with yielded easier…

Probability · Mathematics 2023-05-16 Aolan Ding , Rick Durrett

We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the…

Databases · Computer Science 2016-10-03 Till Schäfer , Petra Mutzel

We prove a moderate deviations principles for the size of the largest connected component in a random $d$-uniform hypergraph. The key tool is a version of the exploration process, that is also used to investigate the giant component of an…

Probability · Mathematics 2019-07-19 Jingjia Liu , Matthias Löwe

We show that, using the Coulomb fluid approach, we are able to derive a rate function $\Psi(c,x)$ of two variables that captures: (i) the large deviations of bulk eigenvalues; (ii) the large deviations of extreme eigenvalues (both left and…

Statistical Mechanics · Physics 2015-10-27 Isaac Pérez Castillo

We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of random networks with arbitrary degree distribution and nonzero clustering introduced in [M.E.J. Newman, Phys.…

Physics and Society · Physics 2013-06-06 Adam Hackett , Sergey Melnik , James P. Gleeson

Recently, van der Hofstad, Komj\'{a}thy, and Vadon (2022) identified the critical point for the emergence of a giant connected component for the bipartite configuration model (BCM) and used this to analyze its associated random intersection…

Probability · Mathematics 2024-10-17 David Clancy

We propose a new model of cluster growth according to which the probability that a new unit is placed in a point at a distance $r$ from the city center is a Gaussian with mean equal to the cluster radius and variance proportional to the…

Physics and Society · Physics 2007-05-23 M. Pica Ciamarra , A. Coniglio

We consider the observability model in networks with arbitrary topologies. We introduce a system of coupled nonlinear equations, valid under the locally tree-like ansatz, to describe the size of the largest observable cluster as a function…

Physics and Society · Physics 2016-09-28 Yang Yang , Filippo Radicchi

We consider the model of random interlacements on $\mathbb{Z}^d$ introduced in Sznitman [Vacant set of random interlacements and percolation (2007) preprint]. For this model, we prove the uniqueness of the infinite component of the vacant…

Probability · Mathematics 2009-03-03 Augusto Teixeira

We study the statistical properties of the generation of random graphs according the configuration model, where one assigns randomly degrees to nodes. This model is often used, e.g., for the scale-free degree distribution ~d^gamma. For the…

Disordered Systems and Neural Networks · Physics 2015-05-28 Hendrike Klein-Hennig , Alexander K. Hartmann

An analytical approach to calculating bond percolation thresholds, sizes of $k$-cores, and sizes of giant connected components on structured random networks with non-zero clustering is presented. The networks are generated using a…

Statistical Mechanics · Physics 2013-06-06 James P. Gleeson , Sergey Melnik

Learning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman \cite{Goodman1949} and Frank \cite{Frank1978}. We revisit a problem formulated by Frank…

Statistics Theory · Mathematics 2019-06-18 Jason M. Klusowski , Yihong Wu

Given a graph $G$, we consider a model for a random cover of $G$ by taking two parallel copies of $G$ and crossing every pair of parallel edges randomly with probability $q$ independently of each other. The resulting graph $G_q$, is a…

Probability · Mathematics 2025-06-03 Paul Drouvillé

The following random graph model was introduced for the evolution of protein-protein interaction networks: Let $\mathcal G = (G_n)_{n=n_0, n_0+1,...}$ be a sequence of random graphs, where $G_n = (V_n, E_n)$ is a graph with $|V_n|=n$…

Probability · Mathematics 2024-07-02 Felix Hermann , Peter Pfaffelhuber

We study the evolution of percolation with freezing. Specifically, we consider cluster formation via two competing processes: irreversible aggregation and freezing. We find that when the freezing rate exceeds a certain threshold, the…

Statistical Mechanics · Physics 2007-05-23 E. Ben-Naim , P. L. Krapivsky

We study the "rank 1 case" of the inhomogeneous random graph model. In the subcritical case we derive an exact formula for the asymptotic size of the largest connected component scaled to log n. This result is new, it completes the…

Probability · Mathematics 2007-06-15 T. S. Turova

We solve the graph bi-partitioning problem in dense graphs with arbitrary degree distribution using the replica method. We find the cut-size to scale universally with <k^1/2>. In contrast, earlier results studying the problem in graphs with…

Disordered Systems and Neural Networks · Physics 2008-12-11 Joerg Reichardt , Stefan Bornholdt
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