scholarly journals Granger-Causality Inference of the Existence of Unobserved Important Components in Network Analysis

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 994
Author(s):  
Heba Elsegai
Keyword(s):  
Network Analysis ◽  
Granger Causality ◽  
Partial Correlation ◽  
Partial Coherence ◽  
Partial Directed Coherence ◽  
Major Interest ◽  
Causality Inference ◽  
Interacting Systems ◽  
Multivariate Systems ◽  
Important Nodes

Detecting causal interrelationships in multivariate systems, in terms of the Granger-causality concept, is of major interest for applications in many fields. Analyzing all the relevant components of a system is almost impossible, which contrasts with the concept of Granger causality. Not observing some components might, in turn, lead to misleading results, particularly if the missing components are the most influential and important in the system under investigation. In networks, the importance of a node depends on the number of nodes connected to this node. The degree of centrality is the most commonly used measure to identify important nodes in networks. There are two kinds of degree centrality, which are in-degree and out-degree. This manuscrpt is concerned with finding the highest out-degree among nodes to identify the most influential nodes. Inferring the existence of unobserved important components is critical in many multivariate interacting systems. The implications of such a situation are discussed in the Granger-causality framework. To this end, two of the most recent Granger-causality techniques, renormalized partial directed coherence and directed partial correlation, were employed. They were then compared in terms of their performance according to the extent to which they can infer the existence of unobserved important components. Sub-network analysis was conducted to aid these two techniques in inferring the existence of unobserved important components, which is evidenced in the results. By comparing the results of the two conducted techniques, it can be asserted that renormalized partial coherence outperforms directed partial correlation in the inference of existing unobserved important components that have not been included in the analysis. This measure of Granger causality and sub-network analysis emphasizes their ubiquitous successful applicability in such cases of the existence of hidden unobserved important components.

A network analysis of political incivility dimensions

2019 ◽  
Vol 4 (3) ◽  
pp. 204-223
Author(s):  
Toby Hopp
Keyword(s):  
Network Analysis ◽  
Partial Correlation ◽  
Precise Definition ◽  
Correlation Network ◽  
Political Discussion ◽  
Relational Dynamics ◽  
Communication Processes ◽  
Democratic Communication ◽  
Scholarly Inquiry

Although online political incivility has increasingly become an object of scholarly inquiry, there exists little agreement on the construct’s precise definition. The goal of this work was therefore to explore the relational dynamics among previously identified dimensions of online political incivility. The results of a regularized partial correlation network indicated that a communicator’s desire to exclude attitude-discrepant others from discussion played an especially influential role in the variable network. The data also suggested that certain facets of incivility may be likely to be deployed together. Specifically, the data suggested the existence of two identifiable groupings of incivility factors: (1) variables pertaining to violation of speech-based norms and (2) variables pertaining to the violation of the inclusion-based norms that underlie democratic communication processes. These results are discussed in the context of political discussion and deliberation.

scholarly journals The impact of latent confounders in directed network analysis in neuroscience

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110612 ◽  
Author(s):  
Rebecca Ramb ◽  
Michael Eichler ◽  
Alex Ing ◽  
Marco Thiel ◽  
Cornelius Weiller ◽  
...  
Keyword(s):  
Network Analysis ◽  
Nonlinear System ◽  
Granger Causality ◽  
Directed Network ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Physiological Pathways ◽  
The Impact ◽  
The Brain ◽  
Past Activity

In the analysis of neuroscience data, the identification of task-related causal relationships between various areas of the brain gives insights about the network of physiological pathways that are active during the task. One increasingly used approach to identify causal connectivity uses the concept of Granger causality that exploits predictability of activity in one region by past activity in other regions of the brain. Owing to the complexity of the data, selecting components for the analysis of causality as a preprocessing step has to be performed. This includes predetermined—and often arbitrary—exclusion of information. Therefore, the system is confounded by latent sources. In this paper, the effect of latent confounders is demonstrated, and paths of influence among three components are studied. While methods for analysing Granger causality are commonly based on linear vector autoregressive models, the effects of latent confounders are expected to be present also in nonlinear systems. Therefore, all analyses are also performed for a simulated nonlinear system and discussed with regard to applications in neuroscience.

scholarly journals DLI: A Deep Learning-Based Granger Causality Inference

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Peng
Keyword(s):  
Deep Learning ◽  
Granger Causality ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Target Variable ◽  
Short Term ◽  
Term Memory ◽  
P Index ◽  
Causality Inference ◽  
Long Short Term Memory

Integrating autoencoder (AE), long short-term memory (LSTM), and convolutional neural network (CNN), we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference (DLI). Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. And the DLI performs a superior prediction accuracy by integrating variables that have causalities with the target variable into the prediction process.

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