Showing papers on "Betweenness centrality published in 2023"

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Abstract: Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

Functional and structural brain network development in children with attention deficit hyperactivity disorder

Abstract: Attention deficit hyperactivity disorder (ADHD) is a prevalent childhood neurodevelopmental disorder. Given the profound brain changes that occur during childhood and adolescence, it is important to examine longitudinal changes of both functional and structural brain connectivity across development in ADHD. This study aimed to examine the development of functional and structural connectivity in children with ADHD compared to controls using graph metrics. One hundred and seventy five individuals (91 children with ADHD and 84 non‐ADHD controls) participated in a longitudinal neuroimaging study with up to three waves. Graph metrics were derived from 370 resting state fMRI (197 Control, 173 ADHD) and 297 diffusion weighted imaging data (152 Control, 145 ADHD) acquired between the ages of 9 and 14. For functional connectivity, children with ADHD (compared to typically developing children) showed lower degree, local efficiency and betweenness centrality predominantly in parietal, temporal and visual cortices and higher degree, local efficiency and betweenness centrality in frontal, parietal, and temporal cortices. For structural connectivity, children with ADHD had lower local efficiency in parietal and temporal cortices and, higher degree and betweenness centrality in frontal, parietal and temporal cortices. Further, differential developmental trajectories of functional and structural connectivity for graph measures were observed in higher‐order cognitive and sensory regions. Our findings show that topology of functional and structural connectomes matures differently between typically developing controls and children with ADHD during childhood and adolescence. Specifically, functional and structural neural circuits associated with sensory and various higher order cognitive functions are altered in children with ADHD.

Smart Transportation Behavior through the COVID-19 Pandemic: A Ride-Hailing System in Iran

Abstract: During the COVID-19 pandemic, significant changes occurred in customer behavior, especially in traffic and urban transmission systems. In this context, there is a need for more scientific research and managerial approaches to develop behavior-based smart transportation solutions to deal with recent changes in customers, drivers, and traffic behaviors, including the volume of traffic and traffic routes. This research has tried to find a comprehensive view of novel travel behavior in different routes using a new social network analysis method. Our research is rooted in graph theory/network analysis and application of centrality concepts in social network analysis, particularly in the ride-hailing transportation systems under monumental competition. In this study, a big city, with near to ten million habitants (Tehran), is considered. All city areas were studied and clustered based on the primary measures of centrality, including degree centrality, Katz centrality, special vector centrality, page rank centrality, proximity centrality, and intermediate centrality. Our data were the trips of this system in Tehran, where the nodes in this network represent Tehran’s districts, and the connection between the two districts indicates the trips made between those two districts. Also, each link’s weight is the number of trips between the two nodes (district). The districts of Tehran were ranked in the smart transportation network based on six criteria: degree centrality, degree centrality of input, degree centrality of output, special vector centrality, hub, and reference points. Finally, according to comprehensive data-driven analysis, the studied company was suggested to create shared value and sustainability through the platform to perform a legitimate system to meet the new challenges. Our proposed system can help managers and governments to develop a behavior-based smart transportation system for big cities.

Computing Betweenness Centrality in Link Streams

Abstract: Betweeness centrality is one of the most important concepts in graph analysis. It was recently extended to link streams, a graph generalization where links arrive over time. However, its computation raises non-trivial issues, due in particular to the fact that time is considered as continuous. We provide here the first algorithms to compute this generalized betweenness centrality, as well as several companion algorithms that have their own interest. They work in polynomial time and space, we illustrate them on typical examples, and we provide an implementation.

Construction and Optimization of an Ecological Network in the Yellow River Source Region Based on MSPA and MCR Modelling

Abstract: The source region of the Yellow River (SRYR) is an important water conservation and farming area in China. Under the dual influence of the natural environment and external pressure, ecological patches in the region are becoming increasingly fragmented, and landscape connectivity is continuously declining, which directly affect the landscape patch pattern and SRYR sustainable development. In the SRYR, morphological spatial pattern analysis (MSPA) and landscape index methods were used to extract ecologically important sources. Based on the minimum cumulative resistance model (MCR), Linkage Mapper was used to generate a potential corridor, and then potential stepped stone patches were identified and extracted by the gravity model and betweenness centrality to build an optimal SRYR ecological network. The distribution of patches in the core area of the SRYR was fragmented, accounting for 80.53% of the total grassland area. The 10 ecological sources based on the landscape connectivity index and 15 important corridors identified based on the MCR model were mainly distributed in the central and eastern regions of the SRYR. Through betweenness centrality, 10 stepped stone patches were added, and 45 planned ecological corridors were obtained to optimize the SRYR ecological network and enhance east and west connectivity. Our research results can provide an important reference for the protection of the SRYR ecosystem, and have important guiding significance and practical value for ecological network construction in ecologically fragmented areas.

Improved centrality measure based on the adapted PageRank algorithm for urban transportation multiplex networks

Abstract: An increasing number of studies have attempted to build a multiplex network (MN) model to generalize the traditional network theory and have proposed various centrality measures for MNs. In this paper, we proposed an improved centrality measure, the Adaptive PageRank Algorithm Modified by the Gravity Model (APAMGM), to identify critical nodes in MNs. We modified the idea that the transition probability of a node is only related to its outgoing connections (or degree in an undirected network) in the adapted PageRank algorithm. In APAMGM, the transition probability is positively correlated with the quality of nodes and inversely correlated with the interaction impedance between nodes. We conducted a case study using a multiplex urban transportation network in Shenzhen, China, which consists of a bus, metro, taxi, and shared bike network. The results show that APAGMG can identify critical nodes with good interpretability and it displays potential for application in networks where spatial interactions exist between nodes. The interdependencies in the network were explored and discussed with the characterization of nodes. This study might provide insights into applying complex network theory and centrality measures to some MNs, especially urban transportation networks.

Exploring My In-Betweenness as a Growing Transnational Scholar through Poetic Autoethnography

Abstract: In this chapter, I present Mystory, a two-part autoethnographic poem about my transnational socialization process, which is imbued with my personal emotions regarding my in-betweenness both spatially and temporally. Also, I refer to Bagga-Gupta's moonrise, sunrise, and earthrise metaphors to express the transformation I have undergone over the years from a monocultural and monolingual teenage language learner into a middle-aged transnational and translingual scholar. I rely on personal memory to recall, to revisit, and to reconstruct my personal experiences in various social settings. I first discuss my early youth which can be summarized as both a literal and a metaphorical movement from East to West, from the small monocultural town of Zonguldak to the multicultural city of İstanbul. Then, I narrate my university experiences as an adolescent, who felt like a misfit on campus due to socio-economic differences between himself and his cohort and the faculty. Finally, I move on to my sojourn in the United States as a middle-aged doctoral student and delve into my feelings of markedness due to my ethnic, religious, and linguistic background.

Comparison of Gamification, Game-Based Learning, and Serious Games in Medical Education: A Scientometrics Analysis.

Abstract: Game in education aims to enhance human motivation and performance in a given activity. Gamification experts and health researchers are still unsure about the status of progress of game for health. So, to fill in this gap, the present study aimed to analyze scientific productions to identify production trends, subject areas, countries, institutes, and authors in these three areas on gamification, game-based learning, and serious games in medical education, as well as to determine co-authorship patterns.The present descriptive quantitative research was conducted through scientometric analysis by using co-authorship networks in gamification, game-based learning, and serious games. First, an advanced search was performed from 1990 to 2020 and the studies were retrieved from Web of sciences, on Aug 17, 2021 The plain text format of data was inputted to Microsoft Excel, CiteSpace and Gephi to analyze scientometric maps for the three domains. Subsequently, the required indicators to review co-authorship networks were obtained: Degree centrality, Betweenness centrality, Closeness centrality, Density, Clustering coefficient, collaboration index and collaboration coefficient.There were 466 documents in gamification, 155 documents in game-based learning, and 295 documents in serious games. The results indicated the rising trend of scientific publications on the three domains. US was a prolific country in all three domains. Author collaboration has remarkably increased, although the number of single-author articles is still high.Due to the increasing growth of publications on these three domains, research can be continued by forming specialized groups and supporting joint publications. Also, research policy-makers should promote author collaborations on the national and international scale.

TriBeC: identifying influential users on social networks with upstream and downstream network centrality

Abstract: The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.

Opening the black box of Library Hi Tech: a social network and bibliometric analysis

Abstract: PurposeThis paper presents a comprehensive study using bibliometric and social network analysis (SNA) to depict the academic community, research hotspots and the correlation between research performance and social network measurements within Library Hi Tech.Design/methodology/approachPublications from Library Hi Tech between 2010 and 2022 are reviewed and analysed through coauthorship analysis, co-occurrence analysis, SNA and the Spearman rank correlation test.FindingsThe annual number of publications in Library Hi Tech increased from 2016 to 2022, indicating that this research has gradually gained global attention. The USA and China are the most significant contributors to the relevant publications. Scholars in this field mainly engage in small-scale cooperation. Academic libraries, digital libraries, libraries, information technology and COVID-19 were hot topics during the study period. In light of the COVID-19 pandemic, there was a marked increase in research on healthcare. Academic interest in the internet of Things and social media has proliferated recently and may soon attract more attention. Spearman rank correlation analysis shows that research performance (i.e. publication count and citation count) is significantly and positively correlated with social network measurements (i.e. degree centrality, betweenness centrality, closeness centrality and eigenvector centrality) in studies of Library Hi Tech.Originality/valueThis paper reveals a systematic picture of the research landscape of Library Hi Tech and provides a potential guide for future research. The relationship between scientific research performance and social network measurements can be objectively identified based on statistical knowledge.

Global structure model modification to improve influential node detection

Abstract: Improving a network's robustness and information acceleration requires assessing the value of its nodes, which has been a central issue in network research. The concept of centrality is crucial since it allows for determining the most important nodes. It is possible to find prominent nodes with the help of centrality indices, but they have computational complexity and are limited by the singularity function. The global structure model (GSM) is one method that helps find these impactful nodes. One of the problems with using GSM is that it ignores these nodes' local information. To address this issue, we propose that considering the features of each index individually and then combining them can result in more accurate detection of influential nodes. An experiment incorporated four attributes: global and local impacts, random walk structure, and node position. In this research, we simulate a real-world network using the SIRIR model to derive its propagation process and then verify its efficacy with measures like the Jaccard similarity score and Kendall's correlation coefficient. According to the findings of the experiments, the Degree of Centrality of the local features has a substantial effect when combined with GSM.

Effectiveness of hierarchical medical system policy: an interrupted time series analysis of a pilot scheme in China

Abstract: Abstract The Chinese healthcare system faces a dilemma between its hospital-centric approach to healthcare delivery and a rapidly ageing population that requires strong primary care. To improve system efficiency and continuity of care, the Hierarchical Medical System (HMS) policy package was issued in November 2014 and fully implemented in 2015 in Ningbo, Zhejiang province, China. This study aimed to investigate the impact of the HMS on the local healthcare system. We conducted a repeated cross-sectional study with quarterly data collected between 2010 and 2018 from Yinzhou district, Ningbo. The data were analysed with an interrupted time series design to assess the impact of HMS on the changes in levels and trends of three outcome variables: primary care physicians’ (PCPs’) patient encounter ratio (i.e. the mean quarterly number of patient encounters of PCPs divided by that of all other physicians), PCP degree ratio (i.e. the mean degree of PCPs divided by the mean degree of all other physicians, with the mean degree revealing the mean activity and popularity of physicians, which reflected the extent to which he/she coordinated with others in delivering health services), and PCP betweenness centrality ratio (i.e. the mean betweenness centrality of PCPs divided by the mean betweenness centrality of all other physicians; the mean betweenness centrality was interpreted as the mean relative importance of physicians within the network, indicating the centrality of the network). Observed results were compared with counterfactual scenarios computed based on pre-HMS trends. Between January 2010 and December 2018, 272 267 patients visited doctors for hypertension, a representative non-communicable disease with a high prevalence of 44.7% among adults aged 35–75 years, amounting to a total of 9 270 974 patient encounters. We analysed quarterly data of 45 464 observations over 36 time points. Compared to the counterfactual, by the fourth quarter of 2018, the PCP patient encounter ratio rose by 42.7% [95% confidence interval (CI): 27.1–58.2, P < 0.001], the PCP degree ratio increased by 23.6% (95%CI: 8.6–38.5, P < 0.01) and the PCP betweenness centrality ratio grew by 129.4% (95%CI: 87.1–171.7, P < 0.001). The HMS policy can incentivize patients to visit primary care facilities and enhance the centrality of PCPs within their professional network.

Evaluation Method for Node Importance of Urban Rail Network Considering Traffic Characteristics

Abstract: As a sustainable means of public transport, the safety of the urban rail transit is a significant section of public safety and is highly important in urban sustainable development. Research on the importance of urban rail stations plays an important role in improving the reliability of urban rail networks. This paper proposed an improved method for evaluating the importance of urban rail stations in a topology network, which was used to identify the key stations that affect the urban rail network performance. This method was based on complex network theory, considering the traffic characteristics of the urban rail network that runs on specific lines and integrating the structural characteristics and interrelationship of the lines where the stations are located. Hereafter, this method will be abbreviated as CLI. In order to verify that the high importance stations evaluated by this method were the key stations that had a great impact on the urban rail network performance, this paper designed a comparative attack experiment of betweenness centrality and CLI. The experiment was carried out by taking the Suzhou Rail Transit (SZRT) network as an example and the largest connected subgraph as well as the network efficiency as indicators to measure the network performance. The results showed that CLI had a greater impact on network performance and could better evaluate the key stations in the urban rail network than node degree and betweenness centrality.

The association of magnetoencephalography high‐frequency oscillations with epilepsy types and a ripple‐based method with source‐level connectivity for mapping epilepsy sources

Abstract: To explore the association between high‐frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.

Bibliometric Analysis of Game Theory on Energy and Natural Resource

Abstract: This paper uses CiteSpace software to conduct a bibliometric analysis of research literature under the topic of game theory which specifically focuses on energy and natural resources in the Web of Science Core Collection. The results show that: since 1990, the number of documents covering the topics of “energy” and “game theory”, and “natural resources” and “game theory” has continued to grow steadily, and entered an explosive growth stage after 2017. In terms of disciplinary classification of published papers, Energy & Fuels has the highest frequency, 311 with a significant centrality, 0.22. In terms of journal publications, Applied Energy is the most cited journal whose frequency is 311 and centrality is 0.01. In terms of country, China has the highest number of published papers, and the United States with the highest overall centrality of papers. North China Electric Power University published 31 papers, the largest number of documents from one institution. In terms of author productivity, Puyan Nie has been the most productive author since 2016. The co-citation cluster analysis on the literature topics shows that the game theory of energy and natural resources have roughly gone through four stages: (1) From 1990 to 2009, this is the embryonic stage with no more than 15 new papers per year; (2) From 2010 to 2014, this stage had microgrid as its mainstream research topic, and other topic clusters officially emerged; (3) From 2015 to 2017, the main research topics became the integrated energy system, subsidy mechanism and household energy management, with a hot topic on the evolutionary game process between government and enterprises; (4) From 2018 to 2021, this stage continued to focus on the previous topics, and the research goes much deeper, resulting in more models and new green technologies. Finally, the keyword analysis concludes with nine themes of concern in this research field, and has come to a comprehensive summary of the mainstream research methods in the field of game theory of energy and natural resources.

The influence of the two-tier network of a regional innovation system on knowledge emergence

Abstract: Purpose This paper aims to focus on the characteristics of a two-tier network featuring internal subject cooperation and external embedded cooperation in the context of regional innovation systems (RISs) and explore the influence of network characteristics on knowledge emergence. Design/methodology/approach Using social network analysis, a two-tier internal and external cooperation network of a RIS is constructed. A negative binomial regression method is used to explore the effects of the characteristics of these two-tier internal and external networks on knowledge emergence, the moderating effect of the cooperation knowledge base in this context is investigated and grouping and quantile regressions are used to conduct heterogeneity analysis. Findings The scale of the internal cooperation network has a positive effect on knowledge emergence, and the betweenness centralization of the internal cooperation network has an inverted U-shaped effect on knowledge emergence. The scale and structural holes of the external embedded network have an inverted U-shaped effect on knowledge emergence. Furthermore, the internal cooperation knowledge base weakens the influence of the external embedded network on knowledge emergence. Practical implications This research may enlighten policymakers with respect to improving the scale and structure of the RIS cooperation network and matching the embedded network based on the internal cooperation knowledge base to promote knowledge emergence. Originality/value This research contributes to the study of knowledge emergence by exploring the influence of a two-tier network structure and scale characteristics on knowledge emergence in RISs. This paper also extends the framework of relevant research by integrating the internal cooperation knowledge base into the analysis of externally embedded cooperation and knowledge emergence.

Structure and robustness of China’s railway transport network

Abstract: ABSTRACT Railway is a fundamental transportation mode for medium and long-distance travel in China. China’s railway transport network (CRTN) has become increasingly complex. Clarifying the structure of the CRTN and its robustness to failures is important for ensuring safe operations. This paper uses train schedule data to construct a railway physical network (RPN) and a train service network (TSN), and proposes a method to simulate the CRTN change processes under different attack strategies, clarify its robustness, and identify its backbone. The results show: First, the RPN is a typical scale-free and small-world network, while the TSN presents a complex hierarchical structure; Second, the RPN is robust to random attacks but vulnerable to targeted attacks, and attacks based on the betweenness centrality as evaluated in the RPN is the most effective mode; Third, the backbone network consists 62 cities including Beijing, Tianjin, and Shijiazhuang.

Modeling the Vulnerability and Resilience of Interdependent Transportation Networks under Multiple Disruptions

Abstract: Because infrastructure systems are highly interconnected, it is crucial to analyze their vulnerability and resilience with the consideration of interdependencies. This paper constructed a bus–metro interdependent network model based on the passenger transfer relationship and used deep learning to identify the network topology attributes. The vulnerability process of the interdependent network to different disruptions under structural and functional perspective was studied. On this basis, this paper adopted a resilience assessment framework and mainly focused on modeling and resilience analysis of interdependent networks’ recovery processes. The optimal and instructive recovery strategy was determined, and it is shown that the increase of the coupling distance cannot alleviate the vulnerability of the interdependent network effectively; after the tolerance coefficient reaches the threshold, the effect on the vulnerability of the dependent network is weakened; and a betweenness-based strategy (BBS) works best in the preferential recovery of key nodes.

Sovereign default network and currency risk premia

Abstract: Abstract We construct a sovereign default network by employing high-dimensional vector autoregressions obtained by analyzing connectedness in sovereign credit default swap markets. We develop four measures of centrality, namely, degree, betweenness, closeness, and eigenvector centralities, to detect whether network properties drive the currency risk premia. We observe that closeness and betweenness centralities can negatively drive currency excess returns but do not exhibit a relationship with forward spread. Thus, our developed network centralities are independent of an unconditional carry trade risk factor. Based on our findings, we develop a trading strategy by taking a long position on peripheral countries’ currencies and a short position on core countries’ currencies. The aforementioned strategy generates a higher Sharpe ratio than the currency momentum strategy. Our proposed strategy is robust to foreign exchange regimes and the coronavirus disease 2019 pandemic.

Spatiotemporal heterogeneity effect of technological progress and agricultural centrality on agricultural carbon emissions in China

Abstract: Reducing agricultural carbon emissions is an important aspect of achieving China’s carbon peak and neutrality goals. Different agricultural centrality result in different agriculture status and role in different regions, affecting agricultural carbon emissions. In this study, agricultural centrality is introduced from the perspective of social network analysis. Spatial autocorrelation analysis, geographically and temporally weighted regression (GTWR) and other methods are used to empirically explore the effect of technological progress and agricultural centrality on the spatiotemporal heterogeneity of agricultural carbon emissions. The moderating effect of agricultural centrality on the relationship between technological progress and agricultural carbon emissions is further explored. The results show that 1) during the research period (2001–2019), the agricultural carbon emissions first increased and then decreased, with remarkable spatial agglomeration characteristics, revealing a significant spatial autocorrelation of carbon emissions among provinces; 2) provinces have distinctly uneven characteristics in the social network of agricultural carbon emissions, while the same province shows relative consistency in terms of location centrality and betweenness centrality. Areas with high centrality are the major grain producing areas, and they invariably play an important role in the spatially linked network of agricultural carbon emissions; 3) technological progress has an inhibitory effect on agricultural carbon emissions, and the regression coefficient decreases from western to eastern regions, demonstrating a spatial gradient distribution. The location centrality has a negative effect on agricultural carbon emissions, with significant spatial heterogeneity. The effect of betweenness centrality on agricultural carbon emissions has increased from positive to negative over time, and the promotion of each province’s intermediary role has inhibited the increase of agricultural carbon emissions; 4) both agricultural location centrality and betweenness centrality have significant positive moderating effects on the relationship between technological progress and agricultural carbon emissions. With the increase of location centrality and betweenness centrality, technological progress has an increasingly strong inhibitory effect on agricultural carbon emissions. We put forward targeted suggestions based on different agricultural centrality in order to reduce agricultural carbon emissions and provide directions for achieving the China’s carbon peak and neutrality goals and the Sustainable Development Goals of the United Nations’ Agenda 2030.

Network Analysis of Internalizing and Externalizing Symptoms in Children and Adolescents.

Abstract: BACKGROUND Experiencing psychological problems during childhood and adolescence is common. However, the detection of behaviors as symptoms of psychopathologies requiring clinical diagnosis and treatment remains low. In order to advance understanding of psychological phenomena and particularly their behavioral manifestations, new theoretical and methodological perspectives such as network analysis are applied. METHOD In the present study, we explore the dynamics of the symptoms of different internalizing, externalizing, and personal-contextual problems using network analysis. We estimate networks of regularized partial correlations, including measures of standard centrality, and the global and structural impact of symptoms on each network. RESULTS The results show that the syndromes we studied are activated through dynamics of symptoms that are strongly related to each other and act as intermediaries of potential psychopathological problems in children and adolescents (e.g., “feels sad,” “worries,” “won’t talk,” “nausea,” “threatens others,” “steals outside”). Centrality measures and impact coefficient ranges were strength (−2.39, 2.05), betweenness (−1.43, 3.38), closeness (−2.60, 2.23), and expected influence (−2.87, 2.13). CONCLUSIONS The results suggest the need to explore attribute dynamics as well as symptomatic comorbidity between them.