Results 11 to 20 of about 5,903,622 (201)
The Random Plots Graph Generation Model for Studying Systems with Unknown Connection Structures
Entropy, 2022 We consider the problem of modeling complex systems where little or nothing is known about the structure of the connections between the elements. In particular, when such systems are to be modeled by graphs, it is unclear what vertex degree distributions
Evgeny Ivanko, Mikhail Chernoskutov
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Physical Review E, 2002 We analyse graphs in which each vertex is assigned random coordinates in a geometric space of arbitrary dimensionality and only edges between adjacent points are present. The critical connectivity is found numerically by examining the size of the largest cluster. We derive an analytical expression for the cluster coefficient which shows that the graphs
Dall, J., Christensen, Michael
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Limit distribution of the size of the giant component in a web random graph [PDF]
Discrete Mathematics & Theoretical Computer Science, 2006 Consider random graph with $N+ 1$ vertices as follows. The degrees of vertices $1,2,\ldots, N$ are the independent identically distributed random variables $\xi_1, \xi_2, \ldots , \xi_N$ with distribution $\mathbf{P}\{\xi_1 \geq k\}=k^{− \tau},$ $k= 1,2,\
Yuri Pavlov
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Limit Theorem for Spectra of Laplace Matrix of Random Graphs
Mathematics, 2023 We consider the limit of the empirical spectral distribution of Laplace matrices of generalized random graphs. Applying the Stieltjes transform method, we prove under general conditions that the limit spectral distribution of Laplace matrices converges ...
Alexander N. Tikhomirov
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Physical Review Letters, 2002 We study the graph coloring problem over random graphs of finite average connectivity $c$. Given a number $q$ of available colors, we find that graphs with low connectivity admit almost always a proper coloring whereas graphs with high connectivity are uncolorable.
R. MULET +3 more
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Graph Convolutional Neural Networks for Web-Scale Recommender Systems [PDF]
Knowledge Discovery and Data Mining, 2018 Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items
Rex Ying +5 more
semanticscholar +1 more source
Random rectangular graphs [PDF]
Physical Review E, 2015 A generalization of the random geometric graph (RGG) model is proposed by considering a set of points uniformly and independently distributed on a rectangle of unit area instead of on a unit square [0,1]^2. The topological properties of the random rectangular graphs (RRGs) generated by this model are then studied as a function of the rectangle sides ...
Estrada, Ernesto, Sheerin, Matthew
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The Surprising Power of Graph Neural Networks with Random Node Initialization [PDF]
International Joint Conference on Artificial Intelligence, 2020 Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph ...
Ralph Abboud +3 more
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Efficient random graph matching via degree profiles [PDF]
Probability theory and related fields, 2018 Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erdős-Rényi graphs G(n,dn)\documentclass[12pt]{minimal ...
Jian Ding +3 more
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Logconcave random graphs [PDF]
Proceedings of the fortieth annual ACM symposium on Theory of computing, 2008 We propose the following model of a random graph on $n$ vertices. Let $F$ be a distribution in $R_+^{n(n-1)/2}$ with a coordinate for every pair $ij$ with $1 \le i,j \le n$. Then $G_{F,p}$ is the distribution on graphs with $n$ vertices obtained by picking a random point $X$ from $F$ and defining a graph on $n$ vertices whose edges are pairs $ij$ for ...
Frieze, Alan +2 more
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