On Scale-Free Prior Distributions and Their Applicability in Large-Scale Network Inference with Gaussian Graphical Models

2009 ◽  
pp. 110-117 ◽  
Author(s):  
Paul Sheridan ◽  
Takeshi Kamimura ◽  
Hidetoshi Shimodaira
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Network Inference ◽  
Gaussian Graphical Models ◽  
Prior Distributions ◽  
Scale Free ◽  
Large Scale Network ◽  
Scale Network

scholarly journals BNMCMC: a software for inferring and visualizing Bayesian networks using MCMC methods

2018 ◽  
Author(s):  
A. K. M. Azad ◽  
Salem A. Alyami ◽  
Jonathan M. Keith
Keyword(s):  
Bayesian Networks ◽  
Biological Networks ◽  
Large Scale ◽  
Network Inference ◽  
Mcmc Methods ◽  
Local Optima ◽  
Large Scale Network ◽  
Software Packages ◽  
Scale Network ◽  
Hit And Run

AbstractMotivationBayesian networks (BNs) are widely used to model biological networks from experimental data. Many software packages exist to infer BN structures, but the chance of getting trapped in local optima is a common challenge. Some recently developed Markov Chain Monte Carlo (MCMC) samplers called the Neighborhood sampler (NS) and Hit-and-Run (HAR) sampler, have shown great potential to substantially avoid this problem compared to the standard Metropolis-Hastings (MH) sampler.ResultsWe have developed a software called BNMCMC for inferring and visualizing BNs from given datasets. This software runs NS, HAR and MH samplers using a discrete Bayesian model. The main advantage of BNMCMC is that it exploits adaptive techniques to efficiently explore BN space and evaluate the posterior probability of candidate BNs to facilitate large-scale network inference.AvailabilityBNMCMC is implemented with C#.NET, ASP.NET, Jquery, Javascript and D3.js. The standalone version (BN visualization missing) available for downloading at https://sourceforge.net/projects/bnmcmc/, where the user-guide and an example file are provided for a simulation. A dedicated BNMCMC web server will be launched soon feature a physics-based BN visualization [email protected]

scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

scholarly journals Large-Scale Network Reduction Towards Scale-Free Structure

2019 ◽  
Vol 6 (4) ◽  
pp. 711-723 ◽  
Author(s):  
Nicolas Martin ◽  
Paolo Frasca ◽  
Carlos Canudas-de-Wit
Keyword(s):  
Large Scale ◽  
Network Reduction ◽  
Scale Free ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Systematic evaluation of normalization methods for glycomics data based on performance of network inference

2019 ◽  
Author(s):  
Elisa Benedetti ◽  
Nathalie Gerstner ◽  
Maja Pučić-Baković ◽  
Toma Keser ◽  
Karli R. Reiding ◽  
...  
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Network Inference ◽  
Systematic Evaluation ◽  
Gaussian Graphical Models ◽  
Data Types ◽  
Experimental Conditions ◽  
Normalization Methods ◽  
Glycosylation Pathway

AbstractGlycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, we here quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms (LC-ESI-MS, UHPLC-FLD and MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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