scholarly journals Calculation of node-pair reliability in large networks with unreliable nodes

1994 ◽  
Vol 43 (3) ◽  
pp. 375-377, 382 ◽  
Author(s):  
D. Torrieri
Keyword(s):  
Large Networks ◽  
Node Pair

scholarly journals Bayesian graphical models for modern biological applications

2021 ◽  
Author(s):  
Yang Ni ◽  
Veerabhadran Baladandayuthapani ◽  
Marina Vannucci ◽  
Francesco C. Stingo
Keyword(s):  
Graphical Models ◽  
Cancer Genomics ◽  
Graph Structure ◽  
Biological Processes ◽  
Limited Sample ◽  
Large Networks ◽  
Bayesian Approaches ◽  
Complex Sampling ◽  
Complex Dependence ◽  
Bayesian Graphical Models

AbstractGraphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

scholarly journals An Algorithm Based on Loop Cutting Contribution Function for Loop Cutset Problem in Bayesian Network

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 462
Author(s):  
Jie Wei ◽  
Yufeng Nie ◽  
Wenxian Xie
Keyword(s):  
Bayesian Inference ◽  
Bayesian Network ◽  
Network Structure ◽  
Computational Efficiency ◽  
Numerical Experiments ◽  
Global Perspective ◽  
Node Pair ◽  
Bayesian Network Structure ◽  
Network Structure Analysis ◽  
Contribution Index

The loop cutset solving algorithm in the Bayesian network is particularly important for Bayesian inference. This paper proposes an algorithm for solving the approximate minimum loop cutset based on the loop cutting contribution index. Compared with the existing algorithms, the algorithm uses the loop cutting contribution index of nodes and node-pairs to analyze nodes from a global perspective, and select loop cutset candidates with node-pair as the unit. The algorithm uses the parameter μ to control the range of node pairs, and the parameter ω to control the selection conditions of the node pairs, so that the algorithm can adjust the parameters according to the size of the Bayesian networks, which ensures computational efficiency. The numerical experiments show that the calculation efficiency of the algorithm is significantly improved when it is consistent with the accuracy of the existing algorithm; the experiments also studied the influence of parameter settings on calculation efficiency using trend analysis and two-way analysis of variance. The loop cutset solving algorithm based on the loop cutting contribution index uses the node-pair as the unit to solve the loop cutset, which helps to improve the efficiency of Bayesian inference and Bayesian network structure analysis.

scholarly journals A review on Queue Management Algorithms in Large Networks

2021 ◽  
Vol 1076 (1) ◽  
pp. 012034
Author(s):  
Mustafa Maad Hamdi ◽  
Hussain Falih Mahdi ◽  
Mohammed Salah Abood ◽  
Ruaa Qahtan Mohammed ◽  
Abdulkareem Dawah Abbas ◽  
...  
Keyword(s):  
Queue Management ◽  
Large Networks ◽  
Management Algorithms

BOOMER: A Tool for Blending Visual P-Homomorphic Queries on Large Networks

2020 ◽  
Author(s):  
Yinglong Song ◽  
Huey Eng Chua ◽  
Sourav S. Bhowmick ◽  
Byron Choi ◽  
Shuigeng Zhou
Keyword(s):  
Large Networks

Dynamic Pricing of Relocating Resources in Large Networks

2019 ◽  
Vol 47 (1) ◽  
pp. 29-30 ◽  
Author(s):  
Santiago R. Balseiro ◽  
David B. Brown ◽  
Chen Chen
Keyword(s):  
Dynamic Pricing ◽  
Large Networks

GraphBoot: Quantifying Uncertainty in Node Feature Learning on Large Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 116-127 ◽  
Author(s):  
Cuneyt Gurcan Akcora ◽  
Yulia R. Gel ◽  
Murat Kantarcioglu ◽  
Vyacheslav Lyubchich ◽  
Bhavani Thuraisingham
Keyword(s):  
Feature Learning ◽  
Large Networks ◽  
Quantifying Uncertainty

scholarly journals Large networks of vertical multi-layer graphenes with morphology-tunable magnetoresistance

Nanoscale ◽  
2013 ◽  
Vol 5 (19) ◽  
pp. 9283 ◽  
Author(s):  
Zengji Yue ◽  
Igor Levchenko ◽  
Shailesh Kumar ◽  
Donghan Seo ◽  
Xiaolin Wang ◽  
...  
Keyword(s):  
Large Networks
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