A Weight-based Information Filtration Algorithm for Stock-correlation Networks

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

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A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Remote Sensing ◽

10.3390/rs13122333 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2333

Author(s):

Lilu Zhu ◽

Xiaolu Su ◽

Yanfeng Hu ◽

Xianqing Tai ◽

Kun Fu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Temporal Correlation ◽

Local Association ◽

Sensing Data ◽

Multi Scale ◽

Correlation Networks ◽

Efficient Integration ◽

Spatio Temporal ◽

Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for hypercholesterolemia

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2021.03.069 ◽

2021 ◽

Vol 553 ◽

pp. 1-8

Author(s):

Ya-nan OuYang ◽

Lu-xin Zhou ◽

Yue-xin Jin ◽

Guo-feng Hou ◽

Peng-fei Yang ◽

...

Keyword(s):

Metabolomics Data ◽

Correlation Networks

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Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0092 ◽

2016 ◽

Vol 16 (6) ◽

pp. 245-255 ◽

Cited By ~ 1

Author(s):

Li Xie ◽

Wenbo Zhou ◽

Yaosen Li

Keyword(s):

Big Data ◽

Recommender System ◽

Recommendation System ◽

Parallel Implementation ◽

Implementation Process ◽

Recommendation Algorithm ◽

Filtration Problem ◽

Information Filtration ◽

Similarity Information ◽

Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

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Region-of-interest reconstruction of motion-contaminated data using a weighted backprojection filtration algorithm

Medical Physics ◽

10.1118/1.2184439 ◽

2006 ◽

Vol 33 (5) ◽

pp. 1222-1238 ◽

Cited By ~ 8

Author(s):

Martin King ◽

Xiaochuan Pan ◽

Lifeng Yu ◽

Maryellen Giger

Keyword(s):

Region Of Interest ◽

Contaminated Data ◽

Filtration Algorithm

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Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments

Bioinformatics and Biomedical Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-319-16480-9_58 ◽

2015 ◽

pp. 607-619 ◽

Cited By ~ 1

Author(s):

Kathryn Dempsey Cooper ◽

Sachin Pawaskar ◽

Hesham H. Ali

Keyword(s):

High Performance Computing ◽

High Performance ◽

Correlation Networks ◽

Computing Environments ◽

Performance Computing

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Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

10.1101/115261 ◽

2017 ◽

Author(s):

Stefano Beretta ◽

Mauro Castelli ◽

Ivo Gonçalves ◽

Ivan Merelli ◽

Daniele Ramazzotti

Keyword(s):

Breast Cancer ◽

Graphical Models ◽

Protein Interactions ◽

Large Scale ◽

Small Sample Size ◽

Small Sample ◽

Cancer Data ◽

Correlation Networks ◽

Model Complex ◽

Cancer Networks

AbstractGene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

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The Replicability and Generalizability of Internalizing Symptom Networks Across Five Samples

10.31234/osf.io/2s6av ◽

2019 ◽

Author(s):

Carter J. Funkhouser ◽

Kelly Correa

Keyword(s):

Network Structure ◽

Internalizing Symptoms ◽

Rank Order ◽

Network Models ◽

Clinical Samples ◽

Cross Sectional ◽

Total N ◽

Correlation Networks ◽

Global Strength ◽

Central Symptom

The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multi-item scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across five samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically-derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all five networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all five samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples.

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