Session details: Special issue on the first ACM SIGMETRICS workshop on large scale network inference (LSNI 2005)

2005 ◽  
Vol 33 (3) ◽  
Keyword(s):  
Large Scale ◽  
Network Inference ◽  
Special Issue ◽  
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]

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

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

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