A comparison of centrality measures and their role in controlling the spread in epidemic networks

The ranking of nodes in a network according to their centrality or "importance” is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it informs the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by these issues, in this paper we review some popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network that takes into account the community-based structure of society. In particular, we use the information of the relevance of individuals in the network to take a control decision, i.e., which individuals should be tested, and possibly quarantined. Finally, we also review the extension of these ranking methods to weighted graphs, and explore the importance of weights in a contact network by exhibiting a toy model and comparing node rankings for this case in the context of disease spread. [ FROM AUTHOR] Copyright of International Journal of Control is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Identification and Analysis of Opinion Leaders for COVID-19 Related Rumor Spreading in Social Media

Since the outbreak of COVID-19, thousands of rumors have occurred on social media, and it is significant to identify opinion leaders who play decisive roles during rumor spreading. However, existing literature lacks such opinion leaders identification and following analysis of COVID-19 background. So this paper takes a COVID-19 case as an example and collects data from Sina Weibo, which is a popular twitter-like social media in China. Then three different centrality measures are applied. Finally, a venn diagram is used to analyze opinion leaders identified, and profiles of them on Weibo are taken into consideration. In conclusion, the paper finds that opinion leaders identified during rumor spreading are institutional and individual accounts with a huge number of followers. But in terms of numbers, government institutions spread information to more people;in terms of breadth, impactful individual accounts deliver more information to more people from all walks of life. © VDE VERLAG GMBH - Berlin - Offenbach.

The impact of COVID-19 on the topological properties of the Moroccan stock market network

This paper investigates the topological evolution of the Casablanca Stock Exchange (CSE) from the perspective of the Coronavirus 2019 (COVID-19) pandemic. Crosscorrelations between the daily closing prices of the Moroccan most active shares (MADEX) index stocks from March 1, 2016 to February 18, 2022 were used to compute the minimum spanning tree (MST) maps. In addition to the whole sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the first year of the COVID-19 pandemic in Morocco. The findings show that, compared to other periods, the mean correlation coefficient increased remarkably through the crisis period;inversely, the mean distance decreased in the same period. The MST and its related tree length support the evidence of the star-like structure, the shrinkage of the MST in times of market turbulence, and an expansion in the recovery period. Besides, the CSE network was less clustered and homogeneous before and after the crisis than in the crisis period, where the banking sector held a key role. The degree and betweenness centrality analysis showed that Itissalat Al-Maghrib and Auto Hall were the most prominent stocks before the crisis. On the other hand, Attijariwafa Bank, Banque Populaire, and Cosumar were the leading stocks during and after the crisis. Indeed, the results of this study can be used to assist policymakers and investors in incorporating subjective judgment into the portfolio optimization problem during extreme events. © Fadwa Bouhlal, Moulay Brahim Sedra, 2022.

Analysis of Correlation based Threshold Networks of Dow Jones stocks of USA: An Econophysics Approach

We have investigated the time series of constituents of the Dow Jones Industrial Average (DJIA) for a period of 18 years (2000-2018). DJIA is a dominant stock market index comprising of thirty US based companies. We have applied the Random Matrix Theory (RMT), complex network analysis and hierarchical clustering techniques to extract out the information from the time series of DJIA stocks. The impact of sub-prime crisis of 2008(FC08) on structure and dynamics of network of DJIA stocks is studied by diving the periods under consideration into three distinct periods;pre crisis (PRC), during crisis (DUC) and post crisis (POC) on the basis of volatility. The RMT analysis shows that data analyzed contain important information. Network analysis reveals high correlation among the stocks in the DUC period. The MST and hierarchical clustering techniques support the results of RMT analysis. Degree centralities, closeness centralities and clustering coefficients of DJIA networks increases in DUC period. High correlation and closeness among stocks in DUC period is depicted in various analyses. The dynamic analysis is also carried out which detect various extreme events such as Covid-19. In conclusion, investigation shows that during the period of crisis, there are significant changes in the structure and dynamics of DJIA network. The findings of investigation can be utilized as risk indicator and detection of such crises in future. © 2022. School of Science, IHU. All rights reserved.

A complex network model for a society with socioeconomic classes.

People's attitudes and behaviors are partially shaped by the socioeconomic class to which they belong. In this work, a model of scale-free graph is proposed to represent the daily personal contacts in a society with three social classes. In the model, the probability of having a connection between two individuals depends on their social classes and on their physical distance. Numerical simulations are performed by considering sociodemographic data from France, Peru, and Zimbabwe. For the complex networks built for these three countries, average values of node degree, shortest-path length, clustering coefficient, closeness centrality, betweenness centrality, and eigenvector centrality are computed. These numerical results are discussed by taking into account the propagation of information about COVID-19.

Study of novel COVID-19 data using graph energy centrality: a soft computing approach

The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A social network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse social networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behaviour. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using graph energy centrality. This measure of centrality allows us to recognise the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel_Corona_Virus_2019_Dataset from Johns Hopkins University to analyse epidemiological data around the world.

The world trade network: country centrality and the COVID-19 pandemic.

International trade is based on a set of complex relationships between different countries that can be modelled as an extremely dense network of interconnected agents. On the one hand, this network might favour the economic growth of countries, but on the other, it can also favour the diffusion of diseases, such as COVID-19. In this paper, we study whether, and to what extent, the topology of the trade network can explain the rate of COVID-19 diffusion and mortality across countries. We compute the countries' centrality measures and we apply the community detection methodology based on communicability distance. We then use these measures as focal regressors in a negative binomial regression framework. In doing so, we also compare the effects of different measures of centrality. Our results show that the numbers of infections and fatalities are larger in countries with a higher centrality in the global trade network.

Analysis of SARS-CoV-2 protein interactome map

By calculating the centrality measures of the nodes of the SARS-CoV-2 protein interactome network, we have identified the viral proteins of potential greatest interest for further experimental investigation to understand the mechanisms by which SARS-CoV-2 attacks cells and to identify possible therapeutic targets. The proteins identified in this study including NSP13, NSP7, ORF3a, ORF8a, and ORF8b, were found to be involved in crucial processes of the viral life cycle, and some of them are currently suspected to be antiviral targets. These results thus demonstrate the importance - and the predictive power- of the in silico analysis of the viral interactome to guide and support experimental investigation, which could otherwise be too complex and time-consuming to carry out in clinical and experimental research, given the size and interaction density of the viral protein network and the current still partial knowledge of this new virus. © 2021 IEEE.

Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI.

Network structures have attracted much interest and have been rigorously studied in the past two decades. Researchers used many mathematical tools to represent these networks, and in recent days, hypergraphs play a vital role in this analysis. This paper presents an efficient technique to find the influential nodes using centrality measure of weighted directed hypergraph. Genetic Algorithm is exploited for tuning the weights of the node in the weighted directed hypergraph through which the characterization of the strength of the nodes, such as strong and weak ties by statistical measurements (mean, standard deviation, and quartiles) is identified effectively. Also, the proposed work is applied to various biological networks for identification of influential nodes and results shows the prominence the work over the existing measures. Furthermore, the technique has been applied to COVID-19 viral protein interactions. The proposed algorithm identified some critical human proteins that belong to the enzymes TMPRSS2, ACE2, and AT-II, which have a considerable role in hosting COVID-19 viral proteins and causes for various types of diseases. Hence these proteins can be targeted in drug design for an effective therapeutic against COVID-19.

An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment.

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

Association between exposure to airborne pollutants and COVID-19 in Los Angeles, United States with ensemble-based dynamic emission model.

This study aims to find the association between short-term exposure to air pollutants, such as particulate matters and ground-level ozone, and SARS-CoV-2 confirmed cases. Generalized linear models (GLM), a typical choice for ecological modeling, have well-established limitations. These limitations include apriori assumptions, inability to handle multicollinearity, and considering differential effects as the fixed effect. We propose an Ensemble-based Dynamic Emission Model (EDEM) to address these limitations. EDEM is developed at the intersection of network science and ensemble learning, i.e., a specialized approach of machine learning. Generalized Additive Model (GAM), i.e., a variant of GLM, and EDEM are tested in Los Angeles and Ventura counties of California, which is one of the biggest SARS-CoV-2 clusters in the US. GAM depicts that a 1 µg/m3, 1 µg/m3, and 1 ppm increase (lag 0-7) in PM 2.5, PM 10, and O3 is associated with 4.51% (CI: 7.01 to -2.00) decrease, 1.62% (CI: 2.23 to -1.022) decrease, and 4.66% (CI: 0.85 to 8.47) increase in daily SARS-CoV-2 cases, respectively. Subsequent increment in lag resulted in the negative association between pollutants and SARS-CoV-2 cases. EDEM results in an R2 score of 90.96% and 79.16% on training and testing datasets, respectively. EDEM confirmed the negative association between particulates and SARS-CoV-2 cases; whereas, the O3 depicts a positive association; however, the positive association observed through GAM is not statistically significant. In addition, the county-level analysis of pollutant concentration interactions suggests that increased emissions from other counties positively affect SARS-CoV-2 cases in adjoining counties as well. The results reiterate the significance of uniformly adhering to air pollution mitigation strategies, especially related to ground-level ozone.