Closeness Centrality Measures Research Articles

Factors associated with the variation and consistency of social network position in group-housed calves

Gregarious animals often choose to interact with other individuals based on characteristics such as sex, age, and body size. However, we know little about the social structure and development of group-housed dairy calves. The aim of this study was to determine factors influencing a calves’ social network position and assess individual consistency over time. Holstein calves were group-housed (9 groups; 10 calves/group) at 15 ± 2.9 d of age (mean ± SD). Calves were weaned over 10 d beginning at 47 ± 2.5 d of age (mean ± SD) and remained in the study until 1 week after weaning. Continuous positional data were recorded within the pen using an ultra-wideband positioning system. Non-directed weighted matrices representing the social networks of calves (heifer: n = 55; bull: n= 32; excluded calves: n = 3) were constructed for each group consisting of consecutive 4-day periods per week over a total of 6 weeks. Social network position was measured using three centrality measures: strength, eigenvector, and closeness. To determine the factors that influence social structure, LMMs were used to test fixed effects of sex, ADG, age at grouping, season (warm or cool), and stage of development (pre-weaning, weaning, and post-weaning), with group and calf ID as nested random factors. Sex, weekly ADG, age at grouping, and season weren’t significant predictors of strength centrality. However, stage of development was a significant predictor for strength (p <.001) and closeness centrality (p <.001), with the highest scores found during the pre-weaning period and lowest during post-weaning. This suggests that calves were associating with more individuals in the early weeks of group formation and were more discerning with social interactions as time went on. The only predictor for eigenvector centrality was season (p =.04), with calves exhibiting higher eigenvector centrality during the cooler months. Strength and closeness centrality measures were significantly repeatable across weekly social networks for all groups (strength: R = 0.31 - R = 0.85, closeness: R = 0.27 - R = 0.89) and consistent across stage of development for 6 of the 9 groups (strength: R = 0.38 - R = 0.80, closeness: R = 0.33 - R = 0.87). Our results suggest that the amount of time spent within a group may play a role in shaping associations in dairy calves, but that dynamics of sociality and network structure may also be driven by individual and life-history traits.

Communicability cosine distance: similarity and symmetry in graphs/networks

A distance based on the exponential kernel of the adjacency matrix of a graph and representing how well two vertices connect to each other in a graph is defined and studied. This communicability cosine distance (CCD) is a Euclidean spherical distance accounting for the cosine of the angles spanned by the position vectors of the graph vertices in this space. The Euclidean distance matrix (EDM) of CCD is used to quantify the similarity between vertices in graphs and networks as well as to define a local vertex invariant—a closeness centrality measure, which discriminate very well vertices in small graphs. It allows to distinguish all nonidentical vertices, also characterizing all identity (asymmetric) graphs–those having only the identity automorphism–among all connected graphs of up to 9 vertices. It also characterizes several other classes of identity graphs. We also study real-world networks in term of both the discriminating power of the new centrality on their vertices as well as in ranking their vertices. We analyze some dictionary networks as well as the network of copurshasing of political books, remarking some of the main advantages of the new approaches studied here.

Evolution of Quantum Computing: Theoretical and Innovation Management Implications for Emerging Quantum Industry

Quantum computing is a vital research field in science and technology. One of the fundamental questions hardly known is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies for economic, industrial, and social change. This study confronts the question here by applying methods of computational scientometrics for publication analyses to explain the structure and evolution of quantum computing research and technologies over a 30-year period. Results reveal that the evolution of quantum computing from 1990 to 2020 has a considerable average increase of connectivity in the network (growth of degree centrality measure), a moderate increase of the average influence of nodes on the flow between nodes (little growth of betweenness centrality measure), and a little reduction of the easiest access of each node to all other nodes (closeness centrality measure). This evolutionary dynamics is due to the increase in size and complexity of the network in quantum computing research over time. This study also suggests that the network of quantum computing has a transition from hardware to software research that supports accelerated evolution of technological pathways in quantum image processing, quantum machine learning, and quantum sensors. Theoretical implications of this study show the morphological evolution of the network in quantum computing from a symmetric to an asymmetric shape driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation management based on R&amp;D investments in new technological directions of quantum computing having a high potential for growth and impact in science and markets.

Analysis of garbage collection network of Colombo district using centrality measures

Waste management is a common problem faced by all developing countries. Colombo city faces the biggest garbage problem than other cities in Sri Lanka. Even through many studies were carried out for waste management problem using different approaches, there were very few research findings were available using graph theoretical approach. In this research, applications of graph theory in garbage collection procedure are depicted. The study mainly focuses on analyzing the garbage collection procedure of Colombo municipal council area in western province through constructing garbage collection network and using centrality measures. Centrality measures are used to compute the importance of any node in a network. Colombo municipal area divides into 6 main administrative districts, and each of these is divided into municipal wards with several junctions and streets. Garbage collection network was initially constructed by assigning a node by a location in google map, and an edge by a street or a path between two locations. Constructed network is an undirected unweighted graph and betweenness, closeness, degree, and eigenvector centrality measures are used to find central locations of the network. By identifying central locations, some machines or recycling trucks can be placed in that central places to deposit the waste. Next, a weighted graph was constructed by taking the weights of an edge as a fraction of weight of collected garbage between two locations. Collected garbage weights and betweenness and degree centrality values for weighted graph are used to identify the shortest paths between central nodes in each municipal ward. Garbage collection trucks can be followed this shortest path in order to reduce their fuel cost and collection time.

Evaluation of Various Topological Indices of Flabellum Graphs

Graph theory serves as an engaging arena for the investigation of proof methods within the field of discrete mathematics, and its findings find practical utility in numerous scientific domains. Chemical graph theory is a specialized branch of mathematics that uses graphs to represent and analyze the structure and properties of chemical compounds. Topological indices are mathematical properties of graphs that play a crucial role in chemistry. They provide a unique way to connect the structural characteristics of chemical compounds to their corresponding molecular graphs. The flabellum graph Fn(k,j) is obtained with the help of k≥2 duplicates of the cycle graph Cn with a common vertex (known as, central vertex). Then, in j of these duplicates, additional edges are added, joining the central vertex to all non-adjacent vertices. In this article, we compute different degree-based topological indices for flabellum graphs, including some well known indices, such as the Randić index, the atom bond connectivity index, the geometric–arithmetic index, and the Zagreb indices. This research provides an in-depth examination of these specific indices within the context of flabellum graphs. Moreover, the behavior of these indices is shown graphically, in terms of the parameters j,k, and n. Additionally, we have extended the concept of the first Zagreb index, to address the issue of cybercrime. This application enables us to identify criminals who exhibit higher levels of activity and engagement in multiple criminal activities when compared to their counterparts. Furthermore, we conducted a comprehensive comparative analysis of the first Zagreb index against the closeness centrality measure. This analysis sheds light on the effectiveness and relevance of the topological index in the context of cybercrime detection and network analysis.

Food Trade Network and Food Security: From the Perspective of Belt and Road Initiative

In recent times, there has been a growing interest in the role of food trade in achieving food security. This study presents a comprehensive analysis of the relationship between food trade and food security within the context of the Belt and Road Initiative (BRI). This study employs complex networks alongside the quadratic assignment procedure (QAP) regression method on trade data of 46 countries under BRI for the period 2008 to 2018. The resulting complex network showed an enhancement in the food trade relationships and food security levels after BRI, indicating a mutually positive effect. In addition, the closeness centrality measure showed a strong correlation with food security, indicating that food trade has positive spillovers on food security in the proximate countries. Using geographical distance as an instrumental variable, the QAP regression model proved the positive effects of food trade on food security. More investment in trade infrastructure is recommended to reduce the negative effect of geographical distance on food security.

A Network Analysis Approach toward Adaptive Overt Narcissism Network.

The relationship between narcissistic personality and art and beauty appreciation has recently become the focus of research investigations. Adaptive narcissists raise their sense of worth in order to shield themselves from harm caused by others. Because they aspire to be more attractive, healthier, and successful versions of themselves, they frequently have greater success in life than the majority of people. Grandiose and overtly narcissistic behavior are the main recognized characteristics of an overt narcissist, which is currently regarded as a personality disorder that puts mental health and wellbeing at peril. On a random sample collection of data from 1101 respondents to an online questionnaire, we conducted a network analysis of the Adaptive Overt Narcissism Scale (AONS) items. In this study, we used a network analysis approach to examine the network structure of adaptive overt narcissism, as well as its relationships with psychological functioning. The present study utilized network analysis to investigate the centrality measures of items in the Adaptive Overt Narcissism Scale (AONS) and their interrelationships. Results indicated that item Q6.8 ("I appreciate art and beauty") had low betweenness, closeness, and strength centrality measures, indicating that it was less influential in the network. However, it also had negative expected influence, suggesting that its absence would have a destabilizing effect on the network. These results highlight the importance of the appreciation of art and beauty in deactivating the adaptive overt narcissist network. Further research is needed to explore the mechanisms underlying this relationship and its implications for narcissism prevention and intervention.

Connecting people to food: A network approach to alleviating food deserts

IntroductionIn 2020, 13.8 million people in the United States struggled with food security. This means they were uncertain whether their food needs would be met. Where someone lives can influence struggles with food security. Food deserts are census tracts that experience high rates of poverty (20 percent of residents at/below poverty thresholds) and low access to grocery stores with nutritious foods. Food deserts and insecurity disproportionately affect disadvantaged communities and may contribute to health issues like diabetes, high blood pressure, and obesity. Public policies can be utilized to lessen the impact of food deserts and one way this can be achieved is through public transit. MethodsWe characterized the role public transportation plays in connecting food desert residents with food by formulating network models from data on food deserts, grocery stores, and public transportation systems for five representative locations: Brown Deer, WI; Lawrence, KS; Albuquerque, NM; Charlotte, NC, and Raleigh, NC. We analyzed these networks by looking at centrality measures, specifically degree and closeness. These centrality measures provide insight on the situation regarding grocery store access for food deserts. ResultsResults of the degree centrality measure varied across study sites; one site (Lawrence) had at least 1 bus stop within 0.25 miles (0.40 km) of the representative address for each food desert. Conversely, two sites (Charlotte and Raleigh) each had 2 representative addresses with 0 bus stops within 0.75 miles (1.21 km). When using the closeness centrality measure, 2 food deserts in Albuquerque had the highest number of grocery stores within 30 min (22 and 9) while 44% of food deserts in Raleigh had 0 grocery stores within 30 min. ConclusionsUsing these results, we identify how public transportation could better connect people with food and offer suggestions to city leaders as a way to help eradicate food deserts.

Visualizing research trends and thematic structure evolution of graphic design

The purpose of this research is to draw a scientific map and show the evolution of thematic structure of world’s graphic design. This is a scientometric study that uses quantitative and indicative methods of network analysis. To draw a scientific map of graphic design research literature, the data was extracted from the Web of Science database within a 2000–20 time period. The retrieved records were formatted using WC10.exe conversion software and then were mapped using Ravar Matrix, Ucinet, Netdraw, VOSviewer software. The findings revealed a growing trend in the number of articles published in graphic design and the most widely used subject areas in the field of graphic were identified. The field of graphic design was mapped based on the closeness centrality measure and its findings revealed that topics such as graphic sign, computer applications, molecular dynamics, computer architecture and graphics processing have the greatest impact on the map.

Identification of familial networks reveals sex-specific density dependence in the dispersal and reproductive success of an endangered ungulate

Density is an important demographic parameter that is commonly overlooked in studies of wild populations. Here, we examined the effects of variable spatially explicit density on a range of demographic parameters in a wild population of a cryptic ungulate, boreal woodland caribou (Rangifer tarandus caribou). Using non-invasive genetic sampling, we applied spatial capture–recapture methods with landscape covariates to estimate the density of boreal woodland caribou across a 108,806 km2 study area. We then created a familial network from the reconstructed parent–offspring relationships to determine whether spatial density influenced sex-specific individual reproductive success, female pregnancy status, and dispersal distance. We found that animal density varied greatly in response to land cover types and disturbance; animal density was most influenced by landscape composition and distance to roads varying from 0 in areas with &amp;gt;20% deciduous cover to 270 caribou per 1,000 km2 in areas presenting contiguous older coniferous cover. We found that both male and female reproductive success varied with density, with males showing a higher probability of having offspring in higher-density areas, and the opposite for females. No differences were found in female pregnancy rates occurring in high- and low-density areas. Dispersal distances varied with density, with offspring moving shorter distances when parents were found in higher-density areas. Familial networks showed lower-closeness centrality and lower-degree centrality for females in higher-density areas, indicating that females found in higher-density areas tend to be less broadly associated with animals across the range. Although high-density areas do reflect good-quality caribou habitat, the observed decreased closeness and degree centrality measures, dispersal rates, and lower female recruitment rates suggest that remnant habitat patches across the landscape may create population sinks.

Dynamic node influence tracking based influence maximization on dynamic social networks

Influence maximization is one of the popular problems in social network analysis. Its application includes viral marketing, epidemic control, and recommender systems. Most of the existing methods are applicable to static networks. However, many real-world networks are dynamic and they evolve with time. This paper studies influence maximization on dynamic social networks and proposes Dynamic Influence based Seed Selection (DYISSE) method. To find ‘k’ most influential nodes, the proposed method first estimates the influence of each node by introducing a Two-hop Triangular Influence (TTI) that measures the influence strength of each node by utilizing the property of triangles. Based on the TTI, a method named the Dynamic Tractable Set (D-TeSt) is proposed to track the changes in the influence of individual nodes when the topology of the network changes with time. The performance of the DYISSE method is analyzed under the LAIC model on two real-world and twelve synthetic dynamic networks. The results show that the proposed method performs better than the temporal versions of DegreeDiscount, MaxDegree, K-shell, Random, and Closeness centrality measures in extracting initial influential nodes.

Anatomising the impact of ResearchGate followers and followings on influence identification

Influence analysis, derived from Social Network Analysis (SNA), is extremely useful in academic literature analytic. Different Academic Social Network Sites (ASNS) have been widely examined for influence analysis in terms of co-authorship and co-citation networks. The impact of other network-based features, such as followers and followings, provided by ASNS such as ResearchGate (RG) and Academia is yet to be anatomised. As proven in ingrained social theories, the followers and followings have significant impact in influence prorogation. This research aims at examining the same in one of the widely adopted ASNS, RG. The rendering process is developed to render real-time RG information, which is modelled into graph. Standard centrality measures are implemented to identify influential users from the constructed RG graph. Each centrality measure gives a list of top- k influential RG users. The results are compared with RGScore and Total Research Interest (TRI) to discover the most effective centrality measure. Betweenness and closeness centrality measures have shown the outperforming results compared with others. A procedure is established to discover influential RG users that are commonly present in all top- k centrality results to identify dominant skills, affiliations, departments and locations from the rendered data.

Towards Enhancing the Robustness of Scale-Free IoT Networks by an Intelligent Rewiring Mechanism.

The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimize the network R against MAs. In this mechanism, edge rewiring is performed between the high and low degree nodes to make a robust network. The Closeness Centrality (CC) measure is utilized to determine the central nodes in the network. Based on the measure, MAs are performed on nodes to damage the network. Therefore, the connections of the neighboring nodes in the network are greatly affected by removing the central nodes. To analyze the network connectivity against the removal of nodes, the performance of CC is found to be more efficient in terms of computational time as compared to Betweenness Centrality (BC) and Eigenvector Centrality (EC). In addition, the Recalculated High Degree based Link Attacks (RHDLA) and the High Degree based Link Attacks (HDLA) are performed to affect the network connectivity. Using the local information of SFN, these attacks damage the vital portion of the network. The INTR outperforms Simulated Annealing (SA) and ROSE in terms of R by 17.8% and 10.7%, respectively. During the rewiring mechanism, the distribution of nodes’ degrees remains constant.

Molecular basis of AR and STK11 genes associated pathogenesis via AMPK pathway and adipocytokine signalling pathway in the development of metabolic disorders in PCOS women

BackgroundOne of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). In recent years, it has been found that insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes mellitus. To explore the differentially expressed genes (DEGs) that regulate these kinds of metabolic risks in PCOS women, we chose the gene expression profile of GSE8157 from the gene expression omnibus (GEO) database.ResultsUsing the GEO2R tool, we identified a total of 339 DEGs between the case and the control sample groups. Gene ontology and Kyoto encyclopedia of gene and genome pathway enrichment analysis were subsequently conducted. High connectivity, betweenness centrality, bottleneck centrality, closeness centrality, and radiality measures were used to rank the ten hub genes. Furthermore, the overlap of these genes resulted in the development of two key genes, AR and STK11. The AMPK and adipocytokine signaling pathways are the two main pathways that these DEGs are involved.ConclusionsThe backbone genes, hub genes and pathways identified would assist us in further exploring the molecular basis of developing risk of type 2 diabetes mellitus in PCOS women and thus provide diagnostic or therapeutic clues.

An effective community-based link prediction model for improving accuracy in social networks

Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds.

P11.01 Symptom networks in glioma: a novel approach to study multidimensional symptomatology in glioma patients

Abstract BACKGROUND Glioma patients experience a high symptom burden contributing to poorer quality of life. Symptoms include depression, cognitive impairment, and fatigue and vary throughout the disease. These symptoms are rarely studied from a comprehensive perspective, while their interdependence may be relevant for their development, perpetuation, and ultimately successful treatment. The emerging field of symptom network analysis uncovers the multidimensional symptom space. Nodes are the symptoms, and edges are operationalized as the full conditional association, or partial correlation, between two symptom severity scores across patients. Highly connected nodes are considered central and may be particularly relevant targets for treatment as disruption of these central nodes impact the entire network. We visualized the overall glioma symptom network, compared multidimensional results to known literature, and statistically compared networks between relevant patient subgroups. MATERIAL AND METHODS A dataset comprised of 355 observations of 180 glioma patients at different disease phases was analysed. Cognitive testing and questionnaires regarding health-related and glioma-specific quality of life, fatigue, depression, and cognition resulted in the definition of 30 symptom nodes. Symptom clusters were visually explored in the resulting networks, as were node strength, betweenness, and closeness centrality measures for each node. Networks were statistically compared between preoperative patients and patients during stable disease, as well as patients with low versus high-grade gliomas. Networks of patients with normal versus severe levels of fatigue were also compared as cancer-/glioma-related fatigue has a strong impact on quality of life and can correlate with other common symptoms such as pain, depression, and/or sleep disturbance. RESULTS Symptom clusters existed between: 1) bodily pain, headache and physical functioning; 2) concentration and motivation; and 3) fatigue and drowsiness. Fatigue and mental health were the most central nodes in the networks. Furthermore, the overall connectivity between symptoms was significantly higher in the severely fatigued patients than in patients with normal fatigue. No network differences were found between low versus high-grade, and preoperative versus stable disease networks. CONCLUSION Fatigue is a central node in glioma patients’ burden of disease, and symptoms are more tightly intercorrelated in patients experiencing severe fatigue. From our data, we hypothesize that fatigue co-exists with or perpetuates other symptoms. Thus, although these results are preliminary, the network approach may innovate hypothesis generation in symptom management.

Probing Conformational Dynamics by Protein Contact Networks: Comparison with NMR Relaxation Studies and Molecular Dynamics Simulations

Protein contact networks (PCNs) have been used for the study of protein structure and function for the past decade. In PCNs, each amino acid is considered as a node while the contacts among amino acids are the links/edges. We examined the possible correlation between the closeness centrality measure of amino acids within PCNs and their mobility as known from NMR spin relaxation experiments and molecular dynamic (MD) simulations. The pivotal observation was that plasticity within a protein stretch correlated inversely to closeness centrality. Effects on protein conformational plasticity caused by the formation of disulfide bonds or protein–protein interactions were also identified by the PCN analysis measure closeness centrality and the hereby introduced percentage of closeness centrality perturbation (% CCP). All the comparisons between PCN measures, NMR data, and MDs were performed in a set of proteins of different biological functions and structures: the core protease domain of anthrax lethal factor, the N-terminal RING domain of E3 Ub ligase Arkadia, the reduced and oxidized forms of human thioredoxin 1, and the ubiquitin molecules (Ub) of the catalytic Ub–RING–E3–E2–Ub complex of E3 ligase Ark2.The graph theory analysis of PCNs could thus provide a general method for assessing the conformational dynamics of free proteins and putative plasticity changes between different protein forms (apo/complexed or reduced/oxidized).

Network analysis and scientific mapping of the field of e-learning in Iran

The main objectives at this research to network analysis and present the scientific map of the field of e-learning in Iran. The importance of this research is to identify emerging and prominent subject areas; and to identify and analyze the co-occurrence network of the e-learning in Iran based on the closeness and betweenness centrality measures. It is a scientometric research, using quantitative method and citation analysis. In order to draw a scientific map of e-learning research literature, the data were extracted from the Web of Science database within a specified time period. The retrieved records were formatted using WC10.exe conversion software, and were then mapped using VOSviewer software. The findings revealed a growing trend in the number of articles published in the field of e-learning, and this number increases on an annual basis. The most widely used subject areas in the field of e-learning were identified. High frequency keywords such as e-learning, education, technology, system and satisfaction are the newly emerging and active concepts in this field. Regarding the widely used subject areas in the field of e-learning, it was noted that the two areas of e-learning and education are ranked first and second as the most frequently used terms. The keywords “technology, system and satisfaction” are the other high frequency terms representing the newly emerging concepts in this field. The field of e-learning was mapped based on the closeness centrality measure and its findings revealed that topics such as learning, technology & system learning have the greatest impact on the map. In contrast, information technology, education technology and learning with the lowest centrality and the least impact.

Influential nodes in planar trusses and meshes using centrality measures

Centrality means the quality or state of staying in the central. Centrality measures in graph theory are used to find the most influential nodes in a graph. Identifying the influential nodes in the truss structures is important to understand the responsible node for design and failure of the structures. Targeting the influential nodes in a structure such as a truss or a mesh is of immense help in optimization of support during actual construction. In this paper, the uses and applications of a planar truss, the elemental clique and skeleton graphs of a Finite Element Mesh (FEM) are discussed. Various sizes of these structures are simulated digitally using python programming language and are analyzed to derive a general formula in order to find the most influential nodes on the basis of degree, closeness, betweenness and eigenvector centrality measures. The findings of this study can be used to provide decision – making support for the analysis, design and optimization of the truss structures.

한･중･일 국제항공 네트워크 비교분석: 주요 공항 및 국적 항공사 네트워크를 중심으로

The purpose of this study is to figure out structural differences among aviation networks of Korea, China, and Japan through analyzing their major airports’ and air carriers’ networks in the international aviation market. This study employs three (degree, betweeness, closeness) centrality, network density, and assortativity measures in the network analysis, in order to evaluate international connectivity of airports and air carriers operating in the three countries. The main results are as follows. First, ICN (Incheon International Airport) is positioned as a top-tier airport among major airports in Asia, in terms of international connectivity evaluated by the three centrality measures. Also, we confirm that the connectivity of secondary hubs in China and East Asia markets has rapidly increased from 2015 to 2019, according to increasing routes connecting between those regions. Next, through comparing major airports’ networks of the three countries in East Asia, we find that in Korea the centrality gap between ICN and the rest of international airports is larger than gaps among major airports in the counterparts, mainly affected by their hub network structures. However, LCCs (Low Cost Carriers) in Korea have the smaller dependence on ICN than FSCs (Full Service Carriers) according to operating regional airports as their secondary hubs. From the results, we confirm that it is necessary to pursue improving the international connectivity of regional international airports through expanding LCCs’ air networks as a suitable strategy to improve the accessibility to international flights for local residents, as well as to alleviate the imbalance of the national airport system.