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In network theory, Pearson's correlation coefficients are most commonly used to measure the degree assortativity of a network. We investigate the behavior of these coefficients in the setting of directed networks with heavy-tailed degree…

Probability · Mathematics 2014-07-01 Pim van der Hoorn , Nelly Litvak

A great deal of effort has been spent measuring topological features of the Internet. However, it was recently argued that sampling based on taking paths or traceroutes through the network from a small number of sources introduces a…

Disordered Systems and Neural Networks · Physics 2008-04-12 Aaron Clauset , Cristopher Moore

Paper proposes a model of large networks based on a random preferential attachment graph with addition of complete subgraphs (cliques). The proposed model refers to models of random graphs following the nonlinear preferential attachment…

Social and Information Networks · Computer Science 2019-04-05 E. B. Yudin

We find assimpotics for the first $k$ highest degrees of the degree distribution in an evolving tree model combining the local choice and the preferential attachment. In the considered model, the random graph is constructd in the following…

Probability · Mathematics 2016-08-31 Yury Malyshkin

Our goal is to quickly find top $k$ lists of nodes with the largest degrees in large complex networks. If the adjacency list of the network is known (not often the case in complex networks), a deterministic algorithm to find a node with the…

Data Structures and Algorithms · Computer Science 2012-02-16 Konstantin Avrachenkov , Nelly Litvak , Marina Sokol , Don Towsley

We study the spreading dynamics on graphs with a power law degree distribution p_k ~ k^-gamma with 2<gamma<3, as an example of a branching process with diverging reproductive number. We provide evidence that the divergence of the second…

Disordered Systems and Neural Networks · Physics 2009-11-11 Alexei Vazquez

The task for a general and useful classification of the tail behaviors of probability distributions still has no satisfactory solution. Due to lack of information outside the range of the data the tails of the distribution should be…

Probability · Mathematics 2019-07-23 Pavlina Jordanova

We consider the upper tail large deviations of subgraph counts for irregular graphs $\mathrm{H}$ in $\mathbb{G}(n,p)$, the sparse Erd\H{o}s-R\'enyi graph on $n$ vertices with edge connectivity probability $p \in (0,1)$. For $n^{-1/\Delta}…

Probability · Mathematics 2025-04-10 Anirban Basak , Shaibal Karmakar

Properties of networks are often characterized in terms of features such as node degree distributions, average path lengths, diameters, or clustering coefficients. Here, we study shortest path length distributions. On the one hand, average…

Social and Information Networks · Computer Science 2015-01-20 Christian Bauckhage , Kristian Kersting , Fabian Hadiji

We consider an infectious disease spreading along the edges of a network which may have significant clustering. The individuals in the population have heterogeneous infectiousness and/or susceptibility. We define the out-transmissibility of…

Populations and Evolution · Quantitative Biology 2008-05-01 Joel C. Miller

Spreading of either information or matter can often be treated as a network problem. It can be of great importance to be able to estimate the likelihood that spreading through a network reaches essentially the entire network while still not…

Disordered Systems and Neural Networks · Physics 2009-03-09 Tomas Alarcon , Henrik Jeldtoft Jensen

We provide large deviations estimates for the upper tail of the number of triangles in scale-free inhomogeneous random graphs where the degrees have power law tails with index $-\alpha, \alpha \in (1,2)$. We show that upper tail…

Probability · Mathematics 2024-03-25 Clara Stegehuis , Bert Zwart

A power law degree distribution is established for a graph evolution model based on the graph class of k-trees. This k-tree-based graph process can be viewed as an idealized model that captures some characteristics of the preferential…

Discrete Mathematics · Computer Science 2008-11-27 Yong Gao

Heavy-tailed networks are often characterized in the literature by their degree distribution's similarity to a power law. However, many heavy-tailed networks in real life do not have power-law degree distributions, and in many applications…

Physics and Society · Physics 2021-07-07 Scott A. Hill

We introduce a random graph model based on k-trees, which can be generated by applying a probabilistic preferential attachment rule, but which also has a simple combinatorial description. We carry out a precise distributional analysis of…

Combinatorics · Mathematics 2010-03-02 Alois Panholzer , Georg Seitz

Analytical results are presented for the structure of networks that evolve via a preferential-attachment-random-deletion (PARD) model in the regime of overall network growth and in the regime of overall contraction. The phase transition…

Statistical Mechanics · Physics 2025-01-13 Barak Budnick , Ofer Biham , Eytan Katzav

This article discusses the properties of extremes of degree sequences calculated from network data. We introduce the notion of a normalized degree, in order to permit a comparison of degree sequences between networks with differing numbers…

Methodology · Statistics 2012-10-17 Sofia C. Olhede , Patrick J. Wolfe

We investigate a network model based on an infinite regular square lattice embedded in the Euclidean plane where the node connection probability is given by the geometrical distance of nodes. We show that the degree distribution in the…

Physics and Society · Physics 2008-06-23 Matus Medo , Jan Smrek

The degree distribution is one of the most fundamental graph properties of interest for real-world graphs. It has been widely observed in numerous domains that graphs typically have a tailed or scale-free degree distribution. While the…

Data Structures and Algorithms · Computer Science 2015-11-30 Olivia Simpson , C. Seshadhri , Andrew McGregor

In a random graph, counts for the number of vertices with given degrees will typically be dependent. We show via a multivariate normal and a Poisson process approximation that, for graphs which have independent edges, with a possibly…

Probability · Mathematics 2009-12-25 K. Lin , G. Reinert
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