Results 11 to 20 of about 1,095,825 (329)
, 1997 Revised chapter for new edition of book "The Mathematics of Paul Erd\H{o}s"
Cameron, Peter J.
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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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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 ...
Alan Frieze +2 more
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Randomized graph cluster randomization
Journal of Causal Inference, 2023 Abstract The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz–Thompson (HT) and Hájek estimators of the GATE are unbiased and consistent, respectively, yet known to exhibit ...
Ugander Johan, Yin Hao
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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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Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding
IEEE Access, 2023 Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph convolutional neural networks (GCNs) as encoders to learn the ...
Chengxin Xie +3 more
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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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Rigorous Result for the CHKNS Random Graph Model [PDF]
Discrete Mathematics & Theoretical Computer Science, 2003 We study the phase transition in a random graph in which vertices and edges are added at constant rates. Two recent papers in Physical Review E by Callaway, Hopcroft, Kleinberg, Newman, and Strogatz, and Dorogovstev, Mendes, and Samukhin have computed ...
Rick Durrett
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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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Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
Sensors, 2018 The working⁻sleeping cycle strategy used for sensor nodes with limited power supply in wireless sensor networks can effectively save their energy, but also causes opportunistic node connections due to the intermittent communication mode, which can ...
Guisong Yang, Zhiwei Peng, Xingyu He
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