scholarly journals SG-t-SNE-Π: Swift Neighbor Embedding of Sparse Stochastic Graphs

2019 ◽  
Vol 4 (39) ◽  
pp. 1577 ◽  
Author(s):  
Nikos Pitsianis ◽  
Dimitris Floros ◽  
Alexandros-Stavros Iliopoulos ◽  
Xiaobai Sun
Keyword(s):  
Stochastic Graphs

scholarly journals Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata

2015 ◽  
Vol 23 (01) ◽  
pp. 1-31 ◽  
Author(s):  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Community Structure ◽  
Dominating Set ◽  
Learning Automata ◽  
Random Variables ◽  
Graph Model ◽  
Maximum Clique ◽  
Distribution Functions ◽  
Maximum Clique Problem ◽  
Stochastic Graphs ◽  
Stochastic Graph

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few, should be treated as a stochastic feature. For example, choosing stochastic graph as the graph model of an online social network and defining community structure in terms of clique, and the associations among the individuals within the community as random variables, the concept of stochastic clique may be used to study community structure properties. In this paper maximum clique in stochastic graph is first defined and then several learning automata-based algorithms are proposed for solving maximum clique problem in stochastic graph where the probability distribution functions of the weights associated with the edges of the graph are unknown. It is shown that by a proper choice of the parameters of the proposed algorithms, one can make the probability of finding maximum clique in stochastic graph as close to unity as possible. Experimental results show that the proposed algorithms significantly reduce the number of samples needed to be taken from the edges of the stochastic graph as compared to the number of samples needed by standard sampling method at a given confidence level.

Sampling algorithms for stochastic graphs: A learning automata approach

2017 ◽  
Vol 127 ◽  
pp. 126-144 ◽  
Author(s):  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Learning Automata ◽  
Stochastic Graphs ◽  
Sampling Algorithms

scholarly journals The Beta Cell in Its Cluster: Stochastic Graphs of Beta Cell Connectivity in the Islets of Langerhans

2015 ◽  
Vol 11 (8) ◽  
pp. e1004423 ◽  
Author(s):  
Deborah A. Striegel ◽  
Manami Hara ◽  
Vipul Periwal
Keyword(s):  
Beta Cell ◽  
Islets Of Langerhans ◽  
Stochastic Graphs

scholarly journals Solving Nonlinear Wave Equation Based on Topology

2021 ◽  
Vol 15 ◽  
pp. 1232-1241
Author(s):  
Liang Song ◽  
Guihua Li ◽  
Shaodong Chen
Keyword(s):  
Wave Equation ◽  
Nonlinear Wave ◽  
Nonlinear Wave Equation ◽  
Node Degree ◽  
Solution Flow ◽  
First Order ◽  
Division Theorem ◽  
Stochastic Graphs ◽  
Stochastic Graph ◽  
Threshold Range

A method of solving nonlinear wave equation based on topology is proposed. Firstly, the characteristics of stochastic graph and Scaleless network are compared, and their topological characteristics are analyzed. Because of the existence of a few axis nodes, Scaleless networks have higher average aggregation than those with the same number of airport nodes and connected stochastic graphs. According to the topological structure of nonlinear wave equation, the first-order integral method is used to solve the nonlinear wave equation. According to the first integration, the threshold range is set, and the solution flow is designed in line with the division theorem. The topology of the network is analyzed according to the node degree, aggregation coefficient and reciprocity of the network, so as to verify and analyze. The experimental results show that the application of this method is 98%, which is still effective for the hyperbolic development equation of the same type.

scholarly journals Data Geometry and Extreme Value Distribution

2021 ◽  
Vol 2 (2) ◽  
pp. 06-15
Author(s):  
Mamadou Cisse ◽  
Aliou Diop ◽  
Souleymane Bognini ◽  
Nonvikan Karl-Augustt ALAHASSA
Keyword(s):  
Weibull Distribution ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Data System ◽  
Peaks Over Threshold ◽  
Stochastic Graphs ◽  
Block Maxima ◽  
Extreme Values Theory

In extreme values theory, there exist two approaches about data treatment: block maxima and peaks-over-threshold (POT) methods, which take in account data over a fixed value. But, those approaches are limited. We show that if a certain geometry is modeled with stochastic graphs, probabilities computed with Generalized Extreme Value (GEV) Distribution can be deflated. In other words, taking data geometry in account change extremes distribution. Otherwise, it appears that if the density characterizing the states space of data system is uniform, and if the quantile studied is positive, then the Weibull distribution is insensitive to data geometry, when it is an area attraction, and the Fréchet distribution becomes the less inflationary.

Characterization of Cycle Stochastic Graphs

2003 ◽  
Vol 15 ◽  
pp. 36
Author(s):  
K. Balasubramanian ◽  
V. Parameswaran ◽  
S.B. Rao
Keyword(s):  
Stochastic Graphs

scholarly journals Spaceland Embedding of Sparse Stochastic Graphs

2019 ◽  
Author(s):  
Nikos Pitsianis ◽  
Alexandros-Stavros Iliopoulos ◽  
Dimitris Floros ◽  
Xiaobai Sun
Keyword(s):  
Stochastic Graphs
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