High Degree Centrality Research Articles

Optimization of integrating life cycle cost and systematic resilience for grey-green stormwater infrastructure

The trade-offs for alternative grey-green infrastructure (HGGI) solutions between life cycle cost (LCC) and systematic resilience may impose many limitations in planning and implementation of urban stormwater strategies in high-dense urban catchment. This study presents a generic approach in the context of a multi-objective optimization, with the aim to (1) optimize and design HGGI under various uncertainties, and conflicting objectives (e.g., LCC, technological and operational resilience); and (2) determine the trade-offs between the multi-objectives and degrees of system decentralization in areas with various development intensities. Taking two areas in Guangzhou, China, as case studies, this study shows that decentralized HGGI exhibits economic advantages. However, to maintain a high systematic resilience in high-density catchments, it is often necessary to rely on solutions with high degree of centralization, which may incur higher cost. HGGI is found to perform better in terms of technological resilience under extreme storms, than operational resilience to the uncertainty associated with structure performance. In addition, porous pavement is found to exhibit high potential for wider application compared to bioretention cells as green infrastructure in HGGI. The framework developed in this study could support decision-making for stormwater management via enhancing both hydraulic reliability and hydrologic resilience.

Are evaluative bibliometrics neoliberal? A historical and theoretical problematization

In this article, we problematize the notion that the continuously growing use of bibliometric evaluation can be effectively explained by ‘neoliberal’ ideology. A prerequisite for our analysis is an understanding of neoliberalism as both denoting a more limited set of concrete principles for the organization of society (the narrow interpretation) or as a hegemonic ideology (the broad interpretation). This conceptual framework, as well as brief history of evaluative bibliometrics, provides an analytical framing for our approach, in which four national research evaluation systems are compared: Norway, Russia, Sweden, and the United Kingdom. On basis of an analysis of the rationales for implementing these systems, as well as their specific design, we discuss the existence or non-existence of neoliberal motivations and rationales. Overall, we find that a relatively homogeneous academic landscape, with a high degree of centralization and government steering, appears to be a common feature for countries implementing national evaluation systems relying on bibliometrics. Such characteristics, we argue, may not be inductively understood as neoliberal but as indications of national states displaying strong political steering of its research system. Consequently, if used without further clarification, ‘neoliberalism’ is a concept too broad and diluted to be useful when analyzing the development of research evaluation and bibliometric measures in the past half a century.

Management or technology? Firms' embeddedness in dual talent flow networks and their innovation performance.

This paper examines the complex relationship between different types of talent flow networks and firms' innovation. Based on the social network theory and human capital theory, we divide the talent flow networks into "management talent flow networks" and "technical talent flow networks". The paper then investigates the potential interacting effect and matching effect between the two types of networks when they influence the innovation of firms. The empirical results, which draw from LinkedIn (China) resume data show that: (1) in both management talent flow networks and technical talent flow networks, higher degree of centrality and larger structural hole indexes can enhance firms' innovation performance; (2) there is significant interacting effect between management talent flow networks and technical talent flow networks in their influence on firms' innovation. That is, the interaction between firms' centrality in management talent flow networks and technical talent flow networks, and the interaction between firms' structural hole indexes in the two networks can both enhance their innovation performance; (3) there is also noteworthy matching effect between the two network types. That is, firms with balanced degree centrality (high-high, or low-low) and balanced structural hole indexes (high-high, or low-low) in management talent flow networks and technical talent flow networks exhibit better innovation performance than those with imbalanced degree centrality (high-low, or low-high) and structural hole indexes (high-low, or low-high) in the two networks. This paper contributes to the classification research on talent flow networks, and deepens our understanding of the complex influencing mechanism between talent flow networks and firms' innovation. Moreover, it provides managerial implications for firms to improve innovation performance via talent flow management.

Impact of farmers’ social relationships on productivity: a multiplex network analysis

Abstract An individual’s productivity is strongly related to work- and non-work-related interactions. Thus, the literature on farmers’ productivity often explores single-layer networks that illustrate the single categories of social relationships. In this study, we investigated farmers’ productivity using a multiplex structure underlying social interaction networks. Relational data were obtained from farmers in four different categories of social relationships. The multiplex network was analysed by applying multiplex degree centrality and layer-by-layer comparison. Also, power and role were analysed through the use of external data by determining their intra-layer correlation. The findings show that diverse types of relationships exist together and they positively affect farmers’ productivity in multiple ways and enhance their innovation capacity. Only 6 out of the 73 farmers had high-degree centrality (> 10), with 18–63$\%$ relevance for the six farmers in the two layers—farming advice (FA) and loans (LO) layers—that the farmers considered important to their productivity. These farmers were more likely to be productive and help improve the productivity of others linked to them. Further, 62$\%$ of the edges in the social gathering and personal advice layers were similar, whereas only 3$\%$ of those in the FA and LO layers were similar, confirming the significance of the latter layers. The influence of social structures on farmers’ productivity implies that social connections enhance farmers’ confidence. The external data further confirm that the formation of some links depends on trust and power, whereas others do not.

Do men and women differ in the capability of weaving online social networks: A perspective of gender stereotype activation

This study aims to identify the gender difference in online social networks by comparing the condition that gender label is manifest and gender stereotype can be activated easily, with the condition under which gender is concealed and the activation of stereotype is difficult. The data about the follower-followee networks from 15,118 individual users and 5933 institutional users in Sina Weibo, a popular local social networking service, was used to compare the gender difference. The findings of this study tell a significantly different story with the prior gender studies regarding the disparity of social networks between genders. The higher degree centrality and lower closeness centrality of males demonstrates that males are more likely to enjoy a central position in both the immediate neighborhood and the entire virtual community. The higher betweenness centrality of males suggests that people depend more on males to make connections with other people, therefore males are more powerful than females in “controlling” the formation of ties between random pairs of users. However, females can increase the likelihood of forming a social tie and building up the whole network structure. Furthermore, people prefer to make an online social tie with those who have different gender with self.

The hotspots and publication trends in postoperative delirium: A bibliometric analysis from 2000 to 2020.

BackgroundPostoperative delirium (POD) is a common aging-associated postoperative complication that has received increasing attention in the context of the aging global population and the number of articles published on POD is gradually increasing. This study aimed to quantify the basic information of scholarly publications on POD and identify the most impactful literature, trends, and hotspots in POD research.Materials and methodsWe searched articles on POD through the Science Citation Index Expanded databases published from 2000 to 2020. Bibliographic information, including year, country, authorship, type, journal, funding, affiliations, subject areas, and hotspots, was collected for further analysis.ResultsA total of 2,114 articles on POD from 2000 to 2020 were identified. The highest number of studies (n = 748) were published in the United States, comprising the most total citations (13,928), followed by China (n = 278), and Germany (n = 209). Inouye, Sharon K. was the most productive author, with 66 publications on POD. The Journal of the American Geriatrics Society published the highest number of articles (n = 80), with the most total citations (4,561) and average (57.01), followed by Anesthesia and Analgesia (n = 52), and the British Journal of Anaesthesia (n = 43). Harvard University was the most productive institute, with the highest H-index (n = 46) and highest degree centrality (n = 191). The top hotspots in the field of POD during this period were “elderly,” “cardiac surgery,” “cognitive impairment,” “hip fracture,” and “intensive care unit.”ConclusionThis study provides an overview of developments in the field of POD over the past 20 years using bibliometric analysis. Overall, research on POD has flourished worldwide. The United States (US) has a relatively high academic impact owing to its productive expertise and institutions in this field. Despite much research illustrating the diagnosis and management of POD in clinical practice, more basic research is needed.

High-Speed Rail Network Structural Characteristics and Evolution in China

Based on high-speed rail (HSR) network data from 2008 to 2020, this study explores the structural characteristics and evolution of China’s HSR network from the perspective of the overall network and urban node network centrality. We show that the overall connectivity of the HSR network has improved significantly, whereas the accessibility of the HSR network has improved slightly. Furthermore, both the density and accessibility of the HSR network in different regions gradually show a decreasing trend from the east coast zone to the southwest. We also find that from the perspective of urban node network centrality, cities with high degree centrality and high betweenness centrality are densely distributed along the northern coast, eastern coast, as well as middle reaches of both the Yellow and Yangtze Rivers. Finally, the node cities have shown a significant increase in both degree centrality and betweenness centrality; thus, both the hub role and radiation capacity have improved. Our study suggests that the government should closely monitor the development of HSR networks in the western region.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis.

BackgroundArtificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.MethodsAll literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.ResultsBeginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.ConclusionResearches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

Temporal-Spatial Analysis of the Essentiality of Hub Proteins in Protein-Protein Interaction Networks

Hubs are generally defined as nodes with a high degree centrality, and they are important for maintaining the stability of complex networks. Previous studies have shown that hub proteins tend to be essential in protein-protein interaction (PPI) networks, providing us with a new way to analyze the essentiality of proteins. Unfortunately, most of the existing studies leverage static PPI networks that are both incomplete and noisy and ignore the temporal and spatial characteristics of PPI networks. Benefiting from the development of high-throughput technologies, abundant multi-biological datasets have been accumulated and can be used for network analysis. To reexamine the relationship between the network centrality and protein essentiality in PPI networks, in this study, we integrated PPI networks with gene expression data and subcellular localization information to construct temporal-spatial dynamic PPI networks. Based on the constructed temporal-spatial dynamic PPI networks, we introduced the maximum degree centrality (MDC) method to evaluate the essentiality of hub proteins. Our results illustrate that the integration of gene expression data or subcellular localization information can significantly reduce noise effects and improve the identification accuracy of essential proteins through the temporal-spatial analysis with disparate sources of PPI networks. Moreover, we redefined hubs and classified them into two types: temporospatial hubs and static hubs. The results show that temporospatial hub proteins are more likely to be essential.

The structure and information spread capability of the network formed by integrated fitness apps

PurposeIn this article we aim to understand how the network formed by fitness tracking devices and associated apps as a subset of the broader health-related Internet of things is capable of spreading information.Design/methodology/approachThe authors used a combination of a content analysis, network analysis, community detection and simulation. A sample of 922 health-related apps (including manufacturers' apps and developers) were collected through snowball sampling after an initial content analysis from a Google search for fitness tracking devices.FindingsThe network of fitness apps is disassortative with high-degree nodes connecting to low-degree nodes, follow a power-law degree distribution and present with low community structure. Information spreads faster through the network than an artificial small-world network and fastest when nodes with high degree centrality are the seeds.Practical implicationsThis capability to spread information holds implications for both intended and unintended data sharing.Originality/valueThe analysis confirms and supports evidence of widespread mobility of data between fitness and health apps that were initially reported in earlier work and in addition provides evidence for the dynamic diffusion capability of the network based on its structure. The structure of the network enables the duality of the purpose of data sharing.

TOOLS TO BALANCE SUB-FEDERAL BUDGETS

The article considers the issues of balancing the sub-federal budget as an integral part of the budget system of the federal state. The Budget Code of the Russian Federation establishes the principle of balance, which enshrines the obligation to adopt a deficit-free budget and, if necessary, to determine the volume and sources of financing its deficit. However, a serious methodological and organisational problem is balancing the budget at the stage of implementation. The high degree of centralisation of fiscal powers in the federal centre, limited influence on the composition of sub-federal budgets and, consequently, on the balance of revenue and expenditure parts, predetermines the objective need to develop a concept of balancing the sub-federal budget. The purpose of this study is to identify and justify the tools of tax revenue management as a main source of budget revenues and an element of the multilevel concept of balancing the sub-federal budget. The methodological basis of this study is the systematic approach, methods of grouping, analysis and comparison. The works of Russian scientists on fiscal policy, balancing budgets, official data of Rosstat and the Federal Treasury were used as the information-empirical base. The result is the development of individual tools, ensuring a balanced budget and lying in the sphere of taxation management at the sub-federal level. The peculiarity of the proposed instruments is that they are within the competence of the authorities of the subjects of the Russian Federation, and their implementation does not require additional funding and does not entail additional administrative burden.

Key Assessment Criteria for Organizational BIM Capabilities: A Cross-Regional Study

Building information modeling (BIM) is an emerging process for managing the design, construction, operation, and maintenance of a facility. While BIM has developed in diverse aspects, the lack of organizational BIM capabilities remains a barrier to its implementation across the global architecture, engineering, and construction (AEC) industry. Accordingly, AEC organizations need to understand their organizational BIM capabilities and those of other organizations to realize the benefits of implementing BIM. This study examines the key criteria for assessing organizational BIM capabilities across two countries—Malaysia and Iran. For this purpose, the study compares the assessment criteria for BIM capabilities among the two countries based on the following elements: (1) criticality of the criteria; (2) degree of centrality of the criteria; and (3) underlying groups of the criteria. A systematic literature review of 26 articles and semi-structured interviews with BIM professionals provided nineteen criteria. A total of 121 and 126 BIM professionals evaluated the criticality of the criteria through a survey in Malaysia and Iran. The collected data were analyzed using the contextual disparities test (Mann–Whitney U test, Kruskal–Wallis H test, and rank agreement factor), network analysis, and exploratory factor analysis (EFA). The leading key criteria in both countries are “the company has the necessary infrastructure to implement BIM”, “the company has a good attitude towards new technology”, and “the company understands its expertise”. However, the subsequent key criteria differ between countries. Furthermore, while the level of agreement on the ranking of the criteria is at a neutral level, the Mann–Whitney U test indicates that the level of criticality significantly differs between countries for most criteria. There are also changes in the level of criticality of the criteria between countries. Finally, criteria with a high degree of centrality differ between countries. On the contrary, although the criteria slightly differ between countries, the overarching groups of the criteria are similar (i.e., the criteria are related to organizational BIM capabilities and organizational capabilities). Understanding these criteria can help researchers and industry practitioners develop the optimal tool for assessing organizational BIM capabilities for the local industry.

Spatial correlations of land-use carbon emissions in the Yangtze River Delta region: A perspective from social network analysis

The use and transformation of land by humans are the main cause of the increase in the concentration of carbon dioxide in the atmosphere. Using land-use data and socioeconomic statistics for 1995, 2000, 2005, 2010, 2015, and 2018, this study examines the carbon emissions from land use and their intensity in the entire Yangtze River Delta Region. We constructed a spatial correlation network of land-use carbon emissions by using a modified gravity model to analyze the characteristics of the spatial correlations and spillover effects of land-use carbon emissions. Results reveal that: (1) the spatial differences in land-use carbon emissions gradually increased, whereas those in land-use carbon emission intensity gradually narrowed from 1995 to 2018; (2) the high-degree centrality of Shanghai, Wuxi, and Suzhou indicated that they had always played leading roles in the network from 1995 to 2018. Moreover, Shanghai and Wuxi had large land-use carbon radiation ranges and together with Suzhou, Hangzhou, Changzhou, and Nanjing, exhibited above-average betweenness centrality from 1995 to 2018 and strong bridging capabilities across the entire network; (3) the land-use carbon emissions had obvious spatial correlations and spillover effects. Our results can provide a scientific basis from an urban agglomeration perspective for the transformation of China’s current economy into a low-carbon one, as well as the realization of regionally differentiated and coordinated emission reduction.

The influential paradox: Brand and deal content sharing by influencers in friendship networks

Social networking platforms facilitate connections among users and enable them to share marketing content with their friends. We conducted a field data analysis and four lab experiments to gain insights into when and why social hubs (i.e., people with high degree centrality) and social bridges (i.e., people with high betweenness centrality) share content. Study 1 reveals an influential paradox: while social hubs (versus low-degree centrality people) are more likely to share deal content, they are less likely to share brand content. In contrast, social bridges (versus low-betweenness people) are more likely to share brand content, but they are less likely to share deal content. The four lab experiments test the mechanisms which motivate the influencers to share deal (i.e., motivation for self-identity signaling) and brand content (i.e., motivation to help others). This research enriches content marketing literature by offering new perspectives on when and why influencers share certain content.

Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions’ statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson’s correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node’s characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.

Tracking discrepancies between expected and actual flows of tourists in an urban destination: An application of user-generated data

In tourism studies, new means of data collection (e.g., smartphone and Internet) have opened up new opportunities for unveiling spatial patterns at various scales. This paper aims to analyze the discrepancy between actual and expected flows between scenic spots in an urban destination at different distance ranges, on an urban scale, based on the individual travel tips data extracted from online sources. The results based on Nanjing, China, indicate that scenic spots with a high degree of centrality are more important in the network of expected flow compared to actual flow. Actual and expected flows of scenic spots are the consequence of the interaction among various factors, including inflow degree, outflow flow, and distance. Moreover, these flows are distance-sensitive at a low level, with a higher sensitivity of the actual flow. Because of the actual situation during the travel, tourists may adjust the itinerary from the expected flow and avoid the trip between two distant scenic spots due to various constraints. Lastly, implications are provided for destination planning and marketing.

Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by rapid brain cell degeneration affecting different areas of the brain. Hippocampus is one of the earliest involved brain regions in the disease. Modern technologies based on high-throughput data have identified transcriptional profiling of several neurological diseases, including AD, for a better comprehension of genetic mechanisms of the disease. In this study, we investigated differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets of hippocampus of AD patients. The identified DEGs were submitted to Weighted correlation network analysis (WGCNA) and ClueGo to explore genes with a higher degree centrality and to comprehend their biological role. Subsequently, MCODE was used to identify subnetworks of interconnected DEGs. Our study found 40 down-regulated genes and 36 up-regulated genes as consensus DEGs. Analysis of the co-expression network revealed ACOT7, ATP8A2, CDC42, GAD1, GOT1, INA, NCALD, and WWTR1 to be genes with a higher degree centrality. ClueGO revealed the pathways that were mainly enriched, such as clathrin coat assembly, synaptic vesicle endocytosis, and DNA damage response signal transduction by p53 class mediator. In addition, we found a subnetwork of 12 interconnected genes (AMPH, CA10, CALY, NEFL, SNAP25, SNAP91, SNCB, STMN2, SV2B, SYN2, SYT1, and SYT13). Only CA10 and CALY are targets of known drugs while the others could be potential novel drug targets.

Managing diversity: an assessment of the national question in Pakistan

ABSTRACT Modern societies are confronted with a plethora of issues that have far-reaching socio-political ramifications. The negative effects of diversity are primarily the result of state policies. Where societies were not homogenous, attempts were made to bring desperate populations together to advance official nationalist projects. Pakistan, being dominantly a Muslim state, has religio-cultural and ethnic diversity where minority groups have raised their voices against the unjust state’s policies. This study examines how these issues evolved over the course of Pakistan’s history. The paper attempts to answer the question ‘why has Pakistan’s plurality become unmanageable?’ The results demonstrate that a high degree of centralization of authority, the adoption of Urdu as a national language, a sense of domination of the central institutions by the Punjabis, underrepresentation of ethnic minorities in state institutions, and controlled society has aggravated the position of diverse groups in Pakistan, with colossal consequences to national economy and social harmony.

Semantic Network Analysis of Newspaper Articles related to Agro-healing

Background and objective Despite the fact that the COVID-19 pandemic has increased the demand for mental health services, access has been limited, resulting in service gaps and severance. Agro-healing, which is expected to be utilized successfully to promote mental health for both individuals and communities, could be a solution. This study was conducted to provide basic data for revitalizing policies and research related to agro-healing by analyzing the trends in big data of online news articles over the last decade. Methods A total of 2,310 news articles related to agro-healing were collected from January 1, 2012 to December 31, 2021 by crawling Naver News. To extract nouns with practical meaning, the Okt morphological analysis of the KoNLPy module in Python 3.9 was employed. Semantic network analysis was conducted to validate degree centrality, betweenness centrality, and eigenvector centrality in order to understand the centrality and connectivity of significant keywords. The data was visualized using Gephi 0.9.2 by performing CONCOR analysis to generate clusters. Results The keywords with the highest degree centrality were agro-healing, followed by healing, care farm, vitality, RDA, citizens, and rural tourism. Agro-healing, Healing, stress, urban, disabilities, care farm, dementia, and rural area were highest in terms of betweenness centrality. The eigenvector centrality was highest in agro-healing, followed by vitality, healing, care farm, and effect. As a result of the CONCOR analysis, four clusters were identified: ‘agro-healing characteristics’, ‘agro-healing resources’, ‘agro-healing activities’, and ‘agro-healing target and effect’. Conclusion According to the findings, social expectations and need for agro-healing to improve public health became a significant part of the discourses. This research is expected to help determine future research and policy directions, as the vitality of agro-healing continues to provide national welfare services and seek sustainable growth in agricultural and rural areas.

Examination of the theses published in family medicine related to health literacy with social network analysis

Background: In recent years, it has been seen that the concept of health literacy has an increasing interest in academic studies and theses published in Turkey. Within the scope of the study, the theses written on health literacy in the field of family medicine were evaluated. It was aimed to reveal the thematic development and knowledge structure of the theses published in this field.
 Material and Methods: Within the scope of this study, social network analysis was carried out based on the keywords obtained from the theses prepared with the target of health literacy. Theses included in the study were accessed via https://tez.yok.gov.tr/UlusalTezMerkezi/. The studies that include the phrase “health literacy” in the title were selected in the examination scope, and the keywords obtained from these theses were listed.
 Results: It is observed that the concept with the highest degree of centrality and betweenness centrality is “health literacy,” as expected. Following the keyword “health literacy,” the keywords “life quality,” “mortality,” “family medicine,” “vaccine,” and “diabetes mellitus” have the highest degree of centrality, respectively. It is observed that the betweenness centrality values of the “newest vital sign” and “life quality” keywords are quite high compared to the degree centrality values.
 Conclusions: Through this study, researchers can focus on issues that are not emphasized much, create original research questions and contribute to the literature. In addition, it may be important to reveal which concepts are associated with the concept of health literacy through this study.