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The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models ER, WS, BA random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low.
We are given an integer $d$, a graph $G=(V,E)$, and a uniformly random embedding $f : V rightarrow {0,1}^d$ of the vertices. We are interested in the probability that $G$ can be realized by a scaled Euclidean norm on $mathbb{R}^d$, in the sense that
Using three supercomputers, we broke a record set in 2011, in the enumeration of non-isomorphic regular graphs by expanding the sequence of A006820 in Online Encyclopedia of Integer Sequences (OEIS), to achieve the number for 4-regular graphs of orde
We analyze complex networks under random matrix theory framework. Particularly, we show that $Delta_3$ statistic, which gives information about the long range correlations among eigenvalues, provides a qualitative measure of randomness in networks. A
Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Su
A simplified Doppler frequency shift measurement approach based on Serrodyne optical frequency translation is reported. A sawtooth wave with an appropriate amplitude is sent to one phase modulation arm of a Mach-Zehnder modulator in conjunction with