scholarly journals Evolving Network Analysis of S&P500 Components: COVID-19 Influence of Cross-Correlation Network Structure

Entropy ◽  
2021 ◽  
Vol 24 (1) ◽  
pp. 21
Author(s):  
Janusz Miśkiewicz ◽  
Dorota Bonarska-Kujawa
Keyword(s):  
Network Analysis ◽  
Network Structure ◽  
Cross Correlation ◽  
Time Window ◽  
Structural Parameters ◽  
Clustering Coefficient ◽  
Correlation Network ◽  
Optimal Time ◽  
Complex Interactions ◽  
Network Properties

The economy is a system of complex interactions. The COVID-19 pandemic strongly influenced economies, particularly through introduced restrictions, which formed a completely new economic environment. The present work focuses on the changes induced by the COVID-19 epidemic on the correlation network structure. The analysis is performed on a representative set of USA companies—the S&P500 components. Four different network structures are constructed (strong, weak, typically, and significantly connected networks), and the rank entropy, cycle entropy, averaged clustering coefficient, and transitivity evolution are established and discussed. Based on the mentioned structural parameters, four different stages have been distinguished during the COVID-19-induced crisis. The proposed network properties and their applicability to a crisis-distinguishing problem are discussed. Moreover, the optimal time window problem is analysed.

scholarly journals Effect of correlation on the traffic capacity of time-varying communication network

2019 ◽  
Vol 33 (26) ◽  
pp. 1950314 ◽  
Author(s):  
S. Kumari ◽  
A. Singh
Keyword(s):  
Network Topology ◽  
Network Structure ◽  
Traffic Congestion ◽  
Time Instant ◽  
Clustering Coefficient ◽  
Optimal Time ◽  
Time Varying ◽  
Rich Club ◽  
Network Properties ◽  
Major Factors

The network topology and the routing strategy are major factors to affect the traffic dynamics of the network. In this work, we aim to design an optimal time-varying network structure and an efficient route is allocated to each user in the network. The network topology is designed by considering addition, removal and rewiring of links. At each time instant, a new node connects with an existing node based on the degree and correlation with its neighbor. Traffic congestion is handled by rewiring of some congested links along with the removal of the anti-preferential and correlated links. Centrality plays an important role to find the most important node in the network. The more a node is central, the more it can be used for the shortest route of the user pairs and it can be congested due to a large number of data coming from its neighborhood. Therefore, routes of the users are selected such that the sum of the centrality of the nodes appearing in the user’s route is minimum. Thereafter, we analyze the network structure by using various network properties such as the clustering coefficient, centrality, average shortest path, rich club coefficient, average packet travel time and order parameter.

scholarly journals Structural Network Alterations Induced by ART-naive and ART-treated Subjects Infected with HIV

2020 ◽  
Author(s):  
Jiaojiao Liu ◽  
Wei Wang ◽  
Yuanyuan Wang ◽  
Mingming Liu ◽  
Dan Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Immune Suppression ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Cd4 Count ◽  
Hiv Patients ◽  
Regional Characteristics ◽  
History Of ◽  
Network Properties ◽  
Cortical Regions

Abstract BackgroundTo investigate how the structural connectivity altered in cART-treated HIV patients and cART-naïve HIV patients by conducting Network analysis of Diffusion Tensor Imaging (DTI) data.MethodsWe enrolled 22 cART-naïve, 23 cART-treated and 28 normal controls in our current study. Firstly, the DTI imaging data pre-processing was conducted and the asymmetric 90 × 90 matrix for each participant from their DTI data was obtained with the use of PANDA. Then, we applied a graph-theoretical network analysis toolkit, GRETNA v2.0, to calculate metrics such as small-“worldness,” characteristic path length, clustering coefficient, global efficiency, local efficiency, and nodal “betweenness”. Finally, we took comparisons among the three groups to investigate topological alterations, and we also conducted relevant analysis with current CD4 T cell counts and neuropsychological evaluation.ResultsResults (1) the regional characteristics (nodal efficiency) were altered in CART- and CART+ patients predominantly in the frontal cortical regions; (2) changes in various network properties in CART- and CART+ patients were associated with the performance of behavior functions; (3) Reduced network segregation was associated with lower current CD4 count in cART- participants, suggesting that brain network segregation may be adversely affected by a history of greater immune suppression. (4) Hubs redistributed in HIV subjects especially in cART+ patients. Conclusion1) The regional characteristics (nodal efficiency) were altered in cART-naïve and cART-treated patients predominantly in the frontal cortical regions; (2) changes in various network properties in cART-naïve and cART-treated patients were associated with the performance of behavior functions; (3) reduced network segregation was associated with lower current CD4 count in cART-naïve participants, suggesting that brain network segregation may have been adversely affected by a history of greater immune suppression. (4) Hubs redistributed in HIV subjects especially in cART-treated patients.

scholarly journals Network analysis in small-sided and conditioned soccer games

Kinesiology ◽  
2017 ◽  
Vol 49 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Gibson Moreira Praça ◽  
Pablo Juan Greco
Keyword(s):  
Network Analysis ◽  
Training Session ◽  
Clustering Coefficient ◽  
Soccer Players ◽  
Degree Centrality ◽  
Task Condition ◽  
Page Rank ◽  
Numerical Equality ◽  
Network Properties ◽  
Task Conditions

The purpose of this article was to investigate the influence of additional players and playing position on the network properties during 2x4 minutes small-sided and conditioned games (SSCG) in soccer. Eighteen young soccer players (age 16.4±0.7 years), six defenders, six midfielders, and six forwards, voluntarily participated in SSCGs with different task conditions (4vs.3, with an additional player inside the pitch, 3vs.3+2, with two support players at the side of the pitch, and 3vs.3, numerical equality). General (density, total links and clustering coefficient) and individual (degree centrality, degree prestige, and page rank) network properties were analyzed using the SocNetV® software. Results showed higher values of density (F=59.354, p=.001), total links (F=40.951, p=.001), and clustering coefficient (F=21.851, p=.001) during the 4vs.3 SSCG. Besides, midfielders showed higher values of degree centrality than defenders and forwards (F=10.669, p=.001). Midfielders and forwards also showed higher values of degree prestige than defenders (F=5.527, p=.005). These results indicate that both task condition and playing position influence the general and individual network properties during SSCGs. For this reason, it is suggested that both task condition and team composition need to be adjusted to the coaches’ purpose for each training session in order to maximize the possibilities of cooperation among the teammates.

scholarly journals Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaohong Chang ◽  
Haiyun Wang
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Practical Importance ◽  
Small World ◽  
Clustering Coefficient ◽  
Policy Makers ◽  
Scale Free ◽  
Network Properties ◽  
The Cross ◽  
Cross Shareholdings

This study depicts the network morphology of firms which establish ties through cross-shareholdings by the theory of complex network analysis method. It calculates some complex network properties of the cross-shareholdings network and analyzes the evolution law of network structure in nearly 7 years. The network clearly displays small world properties and scale-free properties. The cross-shareholdings network average path length and clustering coefficient is with a small amplitude fluctuation; the network structure is relatively stable. Such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the cross-shareholdings network.

Correlational Methods for Analysis of Dance Movements

2011 ◽  
Vol 29 (supplement) ◽  
pp. 283-304 ◽  
Author(s):  
Timothy R. Brick ◽  
Steven M. Boker
Keyword(s):  
Motion Capture ◽  
Mirror Symmetry ◽  
Cross Correlation ◽  
The Other ◽  
Free Form ◽  
Motion Capture Data ◽  
Complex Interactions ◽  
Dance Movements ◽  
Time Lagged ◽  
Insight Into

Among the qualities that distinguish dance from other types of human behavior and interaction are the creation and breaking of synchrony and symmetry. The combination of symmetry and synchrony can provide complex interactions. For example, two dancers might make very different movements, slowing each time the other sped up: a mirror symmetry of velocity. Examining patterns of synchrony and symmetry can provide insight into both the artistic nature of the dance, and the nature of the perceptions and responses of the dancers. However, such complex symmetries are often difficult to quantify. This paper presents three methods – Generalized Local Linear Approximation, Time-lagged Autocorrelation, and Windowed Cross-correlation – for the exploration of symmetry and synchrony in motion-capture data as is it applied to dance and illustrate these with examples from a study of free-form dance. Combined, these techniques provide powerful tools for the examination of the structure of symmetry and synchrony in dance.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

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