scholarly journals Dynamic interbank network analysis using latent space models

2020 ◽  
Vol 112 ◽  
pp. 103792 ◽  
Author(s):  
Fernando Linardi ◽  
Cees Diks ◽  
Marco van der Leij ◽  
Iuri Lazier
Keyword(s):  
Network Analysis ◽  
Latent Space ◽  
Latent Space Models

scholarly journals Dynamic Interbank Network Analysis Using Latent Space Models

2017 ◽  
Author(s):  
Fernando Linardi ◽  
Cees G. H. Diks ◽  
Marco <!>van der Leij ◽  
Iuri Lazier
Keyword(s):  
Network Analysis ◽  
Latent Space ◽  
Latent Space Models

Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review

2021 ◽  
Vol 30 (1) ◽  
pp. 19-33
Author(s):  
Annis Shafika Amran ◽  
Sharifah Aida Sheikh Ibrahim ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Nurfaten Hamzah ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Linear Models ◽  
Brain Activity ◽  
Generative Models ◽  
Education Management ◽  
Latent Space ◽  
Latent Space Models ◽  
Cost Feasibility ◽  
Medical Healthcare ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

scholarly journals Latent space models for multiview network data

2017 ◽  
Vol 11 (3) ◽  
pp. 1217-1244 ◽  
Author(s):  
Michael Salter-Townshend ◽  
Tyler H. McCormick
Keyword(s):  
Network Data ◽  
Latent Space ◽  
Latent Space Models

Modeling reciprocity in social interactions with probabilistic latent space models

2011 ◽  
Vol 17 (1) ◽  
pp. 1-36 ◽  
Author(s):  
ROXANA GIRJU ◽  
MICHAEL J. PAUL
Keyword(s):  
Gender Differences ◽  
Social Interactions ◽  
Pattern Discovery ◽  
Unsupervised Clustering ◽  
Structural Level ◽  
Latent Space ◽  
Affective Value ◽  
Latent Space Models ◽  
The Web

AbstractReciprocity is a pervasive concept that plays an important role in governing people's behavior, judgments, and thus their social interactions. In this paper we present an analysis of the concept of reciprocity as expressed in English and a way to model it. At a larger structural level the reciprocity model will induce representations and clusters of relations between interpersonal verbs. In particular, we introduce an algorithm that semi-automatically discovers patterns encoding reciprocity based on a set of simple yet effective pronoun templates. Using the most frequently occurring patterns we queried the web and extracted 13,443 reciprocal instances, which represent a broad-coverage resource. Unsupervised clustering procedures are performed to generate meaningful semantic clusters of reciprocal instances. We also present several extensions (along with observations) to these models that incorporate meta-attributes like the verbs' affective value, identify gender differences between participants, consider the textual context of the instances, and automatically discover verbs with certain presuppositions. The pattern discovery procedure yields an accuracy of 97 per cent, while the clustering procedures – clustering with pairwise membership and clustering with transitions – indicate accuracies of 91 per cent and 64 per cent, respectively. Our affective value clustering can predict an unknown verb's affective value (positive, negative, or neutral) with 51 per cent accuracy, while it can discriminate between positive and negative values with 68 per cent accuracy. The presupposition discovery procedure yields an accuracy of 97 per cent.

scholarly journals Deciphering protein evolution and fitness landscapes with latent space models

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinqiang Ding ◽  
Zhengting Zou ◽  
Charles L. Brooks III
Keyword(s):  
Protein Evolution ◽  
Fitness Landscape ◽  
Dimensional Space ◽  
Gaussian Process Regression ◽  
Protein Sequences ◽  
Fitness Landscapes ◽  
Space Representation ◽  
Latent Space ◽  
Low Dimensional ◽  
Latent Space Models

AbstractProtein sequences contain rich information about protein evolution, fitness landscapes, and stability. Here we investigate how latent space models trained using variational auto-encoders can infer these properties from sequences. Using both simulated and real sequences, we show that the low dimensional latent space representation of sequences, calculated using the encoder model, captures both evolutionary and ancestral relationships between sequences. Together with experimental fitness data and Gaussian process regression, the latent space representation also enables learning the protein fitness landscape in a continuous low dimensional space. Moreover, the model is also useful in predicting protein mutational stability landscapes and quantifying the importance of stability in shaping protein evolution. Overall, we illustrate that the latent space models learned using variational auto-encoders provide a mechanism for exploration of the rich data contained in protein sequences regarding evolution, fitness and stability and hence are well-suited to help guide protein engineering efforts.

scholarly journals Fratricide in rebel movements: A network analysis of Syrian militant infighting

2019 ◽  
Vol 56 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Emily Kalah Gade ◽  
Mohammed M Hafez ◽  
Michael Gabbay
Keyword(s):  
Network Analysis ◽  
Civil Wars ◽  
Violent Conflict ◽  
Rebel Groups ◽  
Syrian Civil War ◽  
Latent Space ◽  
Space Modeling ◽  
Using Data ◽  
The Relationship

Violent conflict among rebels is a common feature of civil wars and insurgencies. Yet, not all rebel groups are equally prone to such infighting. While previous research has focused on the systemic causes of violent conflict within rebel movements, this article explores the factors that affect the risk of conflict between pairs of rebel groups. We generate hypotheses concerning how differences in power, ideology, and state sponsors between rebel groups impact their propensity to clash and test them using data from the Syrian civil war. The data, drawn from hundreds of infighting claims made by rebel groups on social media, are used to construct a network of conflictual ties among 30 rebel groups. The relationship between the observed network structure and the independent variables is evaluated using network analysis metrics and methods including assortativity, community structure, simulation, and latent space modeling. We find strong evidence that ideologically distant groups have a higher propensity for infighting than ideologically proximate ones. We also find support for power asymmetry, meaning that pairs of groups of disparate size are at greater risk of infighting than pairs of equal strength. No support was found for the proposition that sharing state sponsors mitigates rebels’ propensity for infighting. Our results provide an important corrective to prevailing theory, which discounts the role of ideology in militant factional dynamics within fragmented conflicts.

scholarly journals Modeling node heterogeneity in latent space models for multidimensional networks

2020 ◽  
Vol 74 (3) ◽  
pp. 324-341
Author(s):  
Silvia D'Angelo ◽  
Marco Alfò ◽  
Thomas Brendan Murphy
Keyword(s):  
Latent Space ◽  
Latent Space Models
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