Network Load Balancing in Teleconferencing Systems

Load balancing techniques are useful for efficient networking systems. In teleconferencing systems, it is not an easy job to balance the loads and obtain efficient performance. The current study tries to suggest a network-based approach for load balancing in teleconferencing systems. The aim is to make use of the concepts of graph theory in practicing and simulating teleconferencing systems. In the suggested approach, each computer in the network is considered as a vertex, an edge will be created between two vertices if they are accessible to each other. The weight of the edge between the two computers specifies the cost of access from one vertex to another. The task of transferring happens between the shortest ways of the two nodes taking into consideration the deadline time of the tasks. In terms of the number of the missed deadline tasks, the proposed approach reflected effectiveness in comparison with other approaches. Using the proposed, it is guarantee to obtain a smooth conferencing among users, which is beneficial for the teleconferencing and e-learning as well. Finally, this proposed method is useful for securing smooth conferencing during further lockdown situation (i.e., COVID situation).

An effective method for epidemic suppression by edge removing in complex network

Since the birth of human beings, the spreading of epidemics such as COVID-19 affects our lives heavily and the related studies have become hot topics. All the countries are trying to develop effective prevention and control measures. As a discipline that can simulate the transmission process, complex networks have been applied to epidemic suppression, in which the common approaches are designed to remove the important edges and nodes for controlling the spread of infection. However, the naive removal of nodes and edges in the complex network of the epidemic would be practically infeasible or incur huge costs. With the focus on the effect of epidemic suppression, the existing methods ignore the network connectivity, leading to two serious problems. On the one hand, when we remove nodes, the edges connected to the nodes are also removed, which makes the node is isolated and the connectivity is quickly reduced. On the other hand, although removing edges is less detrimental to network connectivity than removing nodes, existing methods still cause great damage to the network performance in reality. Here, we propose a method to measure edge importance that can protect network connectivity while suppressing epidemic. In the real-world, our method can not only lower the government's spending on epidemic suppression but also persist the economic growth and protect the livelihood of the people to some extent. The proposed method promises to be an effective tool to maintain the functionality of networks while controlling the spread of diseases, for example, diseases spread through contact networks. Copyright © 2023 Liang, Cui and Zhu.

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.)

Real-time updating of dynamic social networks for COVID-19 vaccination strategies.

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization

This work introduces a simple extension to the recent Cognitive Cascades model of Rabb et al. with modeling of multiple media agents, to begin to investigate how the media ecosystem might influence the spread of beliefs (such as beliefs around COVID-19 vaccination). We perform some initial simulations to see how parameters modeling audience fragmentation, selective exposure, and responsiveness of media agents to the beliefs of their subscribers influence polarization. We find that media ecosystem fragmentation and echo-chambers may not in themselves be as polarizing as initially postulated, in the absence of outside fixed media messages that are polarizing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 and Minimizing Micro-Spatial Interactions

COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).

Percolation of temporal hierarchical mobility networks during COVID-19.

Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

Analytics for WhatsApp chats: tracking and visualising students' collaboration in project teams

COVID-19 and remote learning have accelerated online collaboration. Capturing online collaboration in terms of quantitative and qualitative description of students' interaction to achieve learning outcomes remains a challenge. We introduce a framework for describing and visualising students' interactions in WhatsApp group chat. We present five studies (N = 123, N = 64, N = 106, N = 55, N = 46) in courses taken by mathematics and business students. We found that mathematics students wrote more messages and shorter messages than business students. We also found that average number of words per message correlated with the project mark positively in mathematics but negatively in business courses. We suggest a way to visualise a WhatsApp chat as a network and tested the hypothesis that the centralisation coefficient of this network correlated negatively with the project score. The hypothesis was not confirmed. Implications and suggestions for further study are presented.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.

Influence of Information Blocking on the Spread of Virus in Multilayer Networks.

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals.

Simulation of the Effects of Targeted Immunization on the Peak Number of Infections in Complex Networks

In this paper we study the effect of targeted immunization on the peak number of infections in an epidemic outbreak. For this we extend a previously developed python-based dashboard environment for the time efficient simulation-based study of SIR epidemics spread on complex network topologies, using realistic Continuous Time Markov Chain (CTMC) simulations by means of Gillespie's stochastic simulation algorithm. The new components make it possible to study targeted immunization by means of state-of-the-art methods and to visualize typical paths of infection during the temporal evolution of an epidemic. We show results obtained with different centrality measures (eigenvalue centrality of the adjacency and non-backtracking matrix, degree centrality, and average path length centrality), used in targeted immunization. In the results we focus on studying the peak number of infections (PNI). The PNI is very relevant when it comes to the practical management of an epidemic, as it determines, for instance, the number of intensive care units that are needed to offer an appropriate treatment of the disease in critical cases. However, the PNI has received much less attention in studies than the epidemic threshold, which is more relevant in the early stage of an epidemic. Our example study on classical network topologies reveal that the choice of the centrality measure for targeted immunization as well as the number of targeted nodes will have a strong impact on the drop of the PNI. Our simulation-based results on scale-free Barabasi-Albert networks show that the PNI reduction that can be achieved by using modern centrality metrics such as the non-backtracking eigenvalue drop, can lead to up to 40% lower peaks than those achieved with naïve methods such as degree based immunization (immunization of the biggest node(s)) in case of immunization of 2.5% nodes. These results underpins the crucial importance of the correct choice of the centrality metric in targeted immunization. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Modular structures and the delivery of inpatient care in hospitals: a Network Science perspective on healthcare function and dysfunction.

BACKGROUND: Reinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical human and physical components interacting in diverse ways in varying locations. It is challenging to represent the function and dysfunction of such systems in a scientific manner. We describe a Network Science approach to that dilemma. General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary Network Science. We explore that possibility in a study of Australian hospitals during 2019 and 2020. METHODS: We accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisations inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care. RESULTS: We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identification of community structures confirmed the modular structure of the graphs. Their functional implications were readily identified by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion. DISCUSSION AND CONCLUSIONS: Contemporary Network Science is one of the fastest growing areas of current scientific and technical advance. Network Science confirms the modular nature of healthcare service structures. It holds considerable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

One Year with COVID-19: The Impact on Air Transportation throughout 2020

Air transportation, in particular, has faced unprecedented effects by COVID-19 in terms of flight cancellations and airline bailouts;some argue that the air transportation sector is probably among the hardest hit. In this study, we explore the impact of COVID-19 on air transportation as a networked system throughout the year 2020, while taking the unaffected year 2019 as reference. Exploiting recently developed techniques in data science and network science, we analyzed the temporal evolution of air transportation networks at several scales of fractality, including airports, countries, and continents. Our study provides a comprehensive, data-driven analysis, enhanced with pointers into the recent literature, dissecting the impact of the COVID-19 on aviation as a networked system. It is hoped that this work not only improves understanding of COVID-19, but also gives anchor points on how to better handle future pandemics. © ASCE.

Network Load Balancing in Teleconferencing Systems

Load balancing techniques are useful for efficient networking systems. In teleconferencing systems, it is not an easy job to balance the loads and obtain efficient performance. The current study tries to suggest a network-based approach for load balancing in teleconferencing systems. The aim is to make use of the concepts of graph theory in practicing and simulating teleconferencing systems. In the suggested approach, each computer in the network is considered as a vertex, an edge will be created between two vertices if they are accessible to each other. The weight of the edge between the two computers specifies the cost of access from one vertex to another. The task of transferring happens between the shortest ways of the two nodes taking into consideration the deadline time of the tasks. In terms of the number of the missed deadline tasks, the proposed approach reflected effectiveness in comparison with other approaches. Using the proposed, it is guarantee to obtain a smooth conferencing among users, which is beneficial for the teleconferencing and e-learning as well. Finally, this proposed method is useful for securing smooth conferencing during further lockdown situation (i.e., COVID situation). © 2022 IEEE.

Network methods and design of randomized trials: Application to investigation of COVID-19 vaccination boosters.

Network science methods can be useful in design, monitoring, and analysis of randomized trials for control of spread of infections. Their usefulness arises from the role of statistical network models in molecular epidemiology and in study design. Computational models, such as agent-based models that propagate disease on simulated contact networks, can be used to investigate the properties of different study designs and analysis plans. Particularly valuable is the use of these methods to assess how magnitude and detectability of intervention effects depend on both individual-level and network-level characteristics of the enrolled populations. Such investigation also provides an important approach to assessing consequences of study data being incomplete or measured with error. To address these goals, we consider two statistical network models: exponential random graph models and the more flexible congruence class models. We focus first on an historical use of these methods in design and monitoring of a cluster randomized trial in Botswana to evaluate the effect of combination HIV prevention modalities compared to standard of care on HIV incidence. We then present a framework for the design of a study of booster vaccine effects on infection with, and forward transmission of, SARS-CoV-2 variants. Motivation for the study is driven in part by guidance from the United Kingdom to base approval of booster vaccines with "strain changes" that target variants on results of neutralizing antibody tests and information about safety, but without requiring evidence of clinical efficacy. Using designs informed by our agent-based network models, we show it may be feasible to conduct a trial of novel SARS-CoV-2 vaccines in a single large campus to obtain useful information regarding vaccine efficacy against susceptibility and infectiousness. If needed, the sample size could be increased by extending the study to a small number of campuses. Novel network methods may be useful in developing pragmatic SARS-CoV-2 vaccine trials that can leverage existing infrastructure to reduce costs and hasten the development of results.

Analysis of travel mobility under Covid-19: application of network science

COVID-19 is substantially reshaping the tourism and hospitality industries but studies on the changes in travel behaviour in response to the pandemic are limited. Using tourism big data, this research applies network science analytics to determine behavioural changes in travel mobility of domestic travellers who visited Jeju Island, Korea, from June 2019 to December 2020. The findings reveal significant reductions in the number of trips to a destination but also limited spatial connectivity and diversity in travel flow during the pandemic. A higher intensity of travel mobility to outdoor and coastal areas and shorter travel distances are evident during COVID-19. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Stringency without efficiency is not adequate to combat pandemics

Why during pandemics some countries successfully cope with anti-vaccination sentiment while some others do not is partially because people rarely change their minds, since their views are commonly based not on facts and science, but on our feelings and group affiliations. Here, taking the Corruption Perception Index (CPI) as a proxy for the government's incompetence to combat pandemics, for EU countries we report a finding relevant for policymakers that the higher the country's corruption, the higher the number of COVID deaths alongside the fraction of anti-vaxxers, i.e. people who oppose the vaccination. On average, for a given value of the Stringency Index, which is an indicator serving to estimate the strictness of ‘lockdown style’ policies, we report that the numbers of death cases recorded in relatively more corrupt EU countries are higher than the numbers recorded in relatively less corrupt EU countries. We find no significant trend between the Stringency Index and the number of deaths for the entire set of EU countries, but when less and more corrupt countries are separately analysed, for the former group the higher stringency is positively correlated with the number of deaths, while for the later the trend is surprisingly negatively correlated. We propose a model characterised by a phase transition between poorly and fully vaccinated population, where the government effort to vaccinate the entire population triggers the anti-vaccination sentiment and strong linkage between anti-vaxxers suppressing as a feedback mechanism the government's efforts to accomplish a fully vaccinated population. The longer the anti-vaxxers connections last, the harder the government will change their mind.

Resilience Analysis of Australian Electricity and Gas Transmission Networks

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

Effectiveness of Machine Learning Based Anomaly Detection Algorithms Applied to Defense Contract Financial Data

In fiscal year 2020, the U.S. Army spent nearly $77 billion on contracts. Auditors employ various techniques, including anomaly detection, to select contracts that merit scrutiny. But in a resource-constrained environment, auditors can review only a limited number of contracts. Using data obtained from USAspending.gov, we consider how anomaly detection combined with dimensionality reduction can be used to recommend contracts for investigation. We analyze over 20,000 fixed-price Army contracts between fiscal years 2017 to 2020, using more than one hundred combinations of dimensionality reduction and anomaly detection techniques, and formations of artificial anomalies. A consistent finding is that dimensionality reduction using principal components or autoencoders is not demonstrably beneficial. This finding may be due to the discrete nature of the USAspending.gov data and may not apply to other data sets. The best performance is obtained using isolation forests for anomaly detection without dimensionality reduction.