Centrality Analysis Research Articles

국내 산학협력 양상 연구

Objectives This study aims to review the aspects of domestic industry-academic cooperation by keyword network analysis of web data from 2018 to 2021, focusing on impacts of each performer-government, university, industry-on the industry-academia cooperation(IAC) system before and after the COVID-19 phenomenon.
 Methods Keyword network, keyword centrality, ego network, and topic modelling analysis were conducted to examine 1) each influence from the government, university, industry on IAC system, 2) changes its influence after the COVID-19 phenomenon, and 3) the degree to IAC policies were reflected in the aspects of IAC, analysing 5,000 documents collected from web news and blogs.
 Results As a result of keyword network analysis, university, industry, and support project were found to be the top frequent keywords and also at the center of keywords, showing the characteristics of IAC that industry and universities focus on jointly designing and operating government-supported IAC programs. Analysis of ego networks shows that since COVID-19, industry and government networks have grown in size, while recruitment and field practice networks have decreased in size. Keywords such as job creation, SME support, and new industrial talent training, which are major government industry-academic cooperation policies during the text collection period, were partially derived as major areas in topic modeling analysis, but showed different patterns before and after COVID-19.
 Conclusions Government, university, industry were found to be the key players in determining the aspects of the IAC system, and the most influential subject was university over the past four years, actively cooperating with companies in the educational field through government-led development projects. The government's IAC policy for job creation was also reflected in the IAC aspect, but it seems to be limited by the COVID-19 factor. Implications for the role of government, university, and industry in sustaining IAC are discussed.

A general higher-order supracentrality framework based on motifs of temporal networks and multiplex networks

As the dynamics and complexity of networks evolve, identifying the important nodes in temporal networks (i.e., the relationships change over time) and multiplex networks (i.e., the layers can encode different types of relationships) becomes a core topic in network analysis and data science. However, the existing researches merely focus on the first-order relationships (i.e., the interactions only lie in two nodes) regardless of the higher-order relationships (i.e., the interactions exist among multiple nodes) especially for intralayer node interactions, which has been proved to be significant to affect centralities of nodes in the temporal networks and multiplex networks. Motivated by this, by observing and analyzing the interaction patterns of nodes in the temporal networks and multiplex networks, and considering the importance of triadic closure, first we adopt triangular motifs, and reconstruct the real-world system as temporal networks or multiplex networks fused with higher-order motifs information to encode the interactions among multiple nodes. Then we design a higher-order supracentrality framework to mine critical nodes with higher-order features in temporal networks and multiplex networks. The framework we proposed can characterize how the importance of nodes changes over time or different relationships, and also illustrate a complicated dependency of the higher-order supracentrality on the network topology, tunable weights of interlayer coupling and layer teleportation probability. To demonstrate the effectiveness and superiority of higher-order supracentrality framework based on motifs, we provide theoretical analysis and sufficient experiments on two empirical datasets including temporal networks and multiplex networks, the experimental results show that the higher-order supracentrality framework can identify much more abundant important nodes accurately. More significantly, our framework can provide a unifying foundation for centrality analysis in the temporal networks and multiplex networks, and also serve as a guidance for practical applications.

NETWORK ANALYSIS OF VERBAL COMMUNICATIONS IN THE NOVEL THE MASTER AND MARGARITA BY M. A. BULGAKOV

A network analysis of the structure of verbal communications in one of the most popular Russian novels of the Soviet era The Master and Margarita by M. A. Bulgakov has been carried out. The structure of the novel is complex, i.e. there is “a story within a story”. Moreover, the real-world-characters and the other-world-characters are interacting in the novel. This complex and unusual composition makes the novel especially attractive for a network analysis. In our study, only paired verbal communications (conversations) between explicitly present and acting characters have been taken into account; frontal communications, monologues, off-stage characters as well as expected connections between characters have not been taken into account. Based on a character pair verbal communication matrix, a graph has been constructed, the vertices of which are the characters of the novel, while the edges correspond to the connections between them. Taking only paired verbal communications into account leads to the result that the character network can be described by an ordinary, rather than a directed graph. Since the activity of the conversations was out of our intended scope, the edges have been given no weights. The largest connected component of the graph consists of 76 characters. Centralities, such as degree, betweenness, closeness, eigenvector, and assortativity coefficient were computed to characterize the network. The assortativity coefficient of the network under consideration is negative [Formula: see text], i.e. the network does not demonstrate the properties of a social network. The structure of the communities in the network was also analyzed. In addition to the obvious large communities — the characters from the Yershalaim part of the novel and the characters of the Moscow part — the analysis also revealed a fine structure in the Moscow component. Using the analysis of centralities, a group of main characters has been detected. The central characters of the novel are Koroviev, Margarita, Bezdomny, Woland, Behemoth, Azazello, Bosoi, Warenukha, Master, and Levi Matthew.

Noncanonical electromechanical coupling paths in the cardiac hERG channel.

The human ether-a-go-go-related gene (hERG) codes for a Kv channel involved in the delayed rectifier current that determines the plateau period of the cardiac action potential. During the cardiac action potential, this channel rapidly recovers from inactivation, to cease the action potential. hERG displays marked inward rectification, a mechanism due to an extremely fast pore inactivation and shown not to be related to the activation process. Moreover, hERG operates seamlessly without a covalent interaction between the voltage sensor (VSD) and the pore (PD), suggesting the presence of an alternative communication between VSD and PD, without the S4-S5 linker. Such mechanism has been shown in Shaker K+ channel, a domain-swapped channel, but it is yet to be determined in non-domain-swapped channels, like hERG. Here, we used a combination of mutagenesis and molecular dynamics simulations/network analysis to identify a chain of residues coupling the VSD to PD that involves S4∴S1 and S1∴S5 subunit interfaces. The role of each residue (T425, P426, A527, H562, W563, A565, W568, A614, Y616, F617, and T618) in the identified noncanonical paths was quantified by performing betweenness centrality analysis and tested using electrophysiological assays combined with leucine-scanning mutagenesis. To provide unequivocal evidence of the noncanonical path and the agreement between the computational prediction and experimental data, we plotted the betweenness centrality and the minimal distance of each mutant along the noncanonical paths against the free-energy perturbation of activation and of inactivation. These plots showed a good agreement between computational and experimental results, demonstrating that the residues identified are part of the noncanonical electromechanical coupling in hERG. Altogether, our work identifies the presence of allosteric, noncanonical gating paths in the hERG channel and shed new light on the elusive activation and inactivation mechanisms of non-domain-swapped channels. Support: NIH-GM030376, ERC:803213.

Software JimenaE allows efficient dynamic simulations of Boolean networks, centrality and system state analysis

The signal modelling framework JimenaE simulates dynamically Boolean networks. In contrast to SQUAD, there is systematic and not just heuristic calculation of all system states. These specific features are not present in CellNetAnalyzer and BoolNet. JimenaE is an expert extension of Jimena, with new optimized code, network conversion into different formats, rapid convergence both for system state calculation as well as for all three network centralities. It allows higher accuracy in determining network states and allows to dissect networks and identification of network control type and amount for each protein with high accuracy. Biological examples demonstrate this: (i) High plasticity of mesenchymal stromal cells for differentiation into chondrocytes, osteoblasts and adipocytes and differentiation-specific network control focusses on wnt-, TGF-beta and PPAR-gamma signaling. JimenaE allows to study individual proteins, removal or adding interactions (or autocrine loops) and accurately quantifies effects as well as number of system states. (ii) Dynamical modelling of cell–cell interactions of plant Arapidopsis thaliana against Pseudomonas syringae DC3000: We analyze for the first time the pathogen perspective and its interaction with the host. We next provide a detailed analysis on how plant hormonal regulation stimulates specific proteins and who and which protein has which type and amount of network control including a detailed heatmap of the A.thaliana response distinguishing between two states of the immune response. (iii) In an immune response network of dendritic cells confronted with Aspergillus fumigatus, JimenaE calculates now accurately the specific values for centralities and protein-specific network control including chemokine and pattern recognition receptors.

A Study on Research Trends in 『Audiology and Speech Research』 Using Keyword Network Analysis

Purpose: The purpose of this study was to investigate the research trends of papers published in ‘Audiology and Speech Research’ and to obtain implications for the direction of research.Methods: Data were collected from 2005 to 2022 articles research information sharing service (RISS) which is provided by Korea education and Korea Citation Index (KCI). After extracting and cleansing keywords, the frequency, centrality, and visualization data analysis for keywords were performed using KrKwic & NodeXL programs.Results: According to the results of keyword frequency analysis, ‘hearing aid’ showed the highest frequency, followed by ‘hearing loss’, ‘noise’, and ‘cochlear implant’, etc. As a result of the keyword centrality analysis, it was found that the centrality was particularly high in ‘hearing aid’, ‘hearing loss’, ‘word recognition’, ‘cochlear implant’, ‘sensorineural hearing loss’, ‘auditory training’, ‘aural rehabilitaion’.Conclusion: The results of this study will provide useful data for understanding current research trends in ASR and further suggesting new research directions in the field of audiology and speech pathology.

Research Trends in English Writing: A Semantic Network Analysis

This study explores the shifting landscape of English writing research from 2001 to 2020, utilizing a large-scale, data-driven methodology. Data from 1495 articles published in Korea over these two decades was collected and preprocessed with the Biblio data collector, then analyzed with Netminer. The approach involved four stages of semantic analysis: frequency, centrality, network, and modularity analyses. The findings highlight dynamic shifts in research focus. While keywords like ‘test’, ‘college students’, ‘vocabulary’, and ‘level-based’ remained consistent, the 2010s saw emerging themes like ‘task’, ‘textbook analysis’, ‘corpus analysis,’ 'peer feedback’, and ‘genre-based approach’. Centrality analysis showed that in the 2000s, alongside ‘sentence’, ‘questionnaire’ held a central position with multiple nodes linked to it. In the 2010s, ‘questionnaire’ persisted as a central theme but was joined by ‘relationship.’ Network maps generated with Spring 2D and PFnet depicted these evolving interconnections. In the second period, ‘feedback’ emerged as a central theme, yet directly connected to only two nodes: ‘error’ and ‘peer feedback.’ Modularity analysis identified six research groups in each period, with the ‘questionnaire group’ being most significant in the 2000s and the ‘peer feedback group’ gaining prominence in the 2010s. This research illuminates the evolving trends in English writing research, underscoring the potential of big data-driven approaches to uncover key insights and patterns.

Centrality and connectivity analysis of the European airports: a weighted complex network approach

ABSTRACT This study aims to reveal the structure of the European Airport Network (EAN) using concepts from complex network theory by utilising 2019 passenger data collected from Eurostat. Initially, the EAN was explored by computing connectivity and centrality measures and their correlations. The community structure of the EAN was also examined by modularity maximisation using the relatively new Leiden algorithm. When the network was compared with simulated models, it was observed that the EAN had small-world and scale-free properties. To measure the hub performance of the airports, their binary betweenness centrality was compared with a weighted betweenness measure employing the ratio of geographical distance to passenger traffic between the nodes. A significant difference was observed between the two centrality measures.

Alterations of degree centrality and functional connectivity in classic trigeminal neuralgia.

Recent neuroimaging studies have indicated a wide range of structural and regional functional alterations in patients with classic trigeminal neuralgia (CTN). However, few studies have focused on the intrinsic functional characteristics of network organization in the whole brain. Therefore, the present study aimed to characterize the potential intrinsic dysconnectivity pattern of the whole brain functional networks at the voxel level using the degree centrality (DC) analysis in CTN patients. Thirty-four patients with CTN and twenty-nine well-matched healthy controls (HCs) participated in this study. All subjects underwent resting-state functional magnetic resonance imaging (rs-MRI) examination and clinical and neuropsychologic assessments. DC is a graph theory-based measurement that represents the overall functional connectivity (FC) numbers between one voxel and other brain voxels. We first investigated brain regions exhibiting abnormal DC, and further identified their perturbation on FC with other brain regions using a seed-based FC analysis in patients with CTN. In addition, correlation analyses were performed to evaluate the relationship between the abnormal DC value and clinical variables in CTN patients. Compared with the HCs, the patients with CTN exhibited significantly greater DC values in the right pallidum and right putamen, and lower DC values in the right lingual gyrus, right calcarine sulcus, left paracentral lobule, and left midcingulate cortex. A further seed-based FC analysis revealed that the right lingual gyrus showed decreased FC within the visual network and with other core brain networks, including the sensorimotor network, default mode network, and salience network, relative to HCs. Additionally, the left midcingulate cortex exhibited decreased FC within the middle cingulate cortex and the visual network in CTN patients. Moreover, the DC value in the left midcingulate cortex was negatively correlated with the illness duration. The present study shows that CTN patients exhibited specific functional connectivity network alterations in the basal ganglia, visual network, and salience network, which may reflect the aberrant neural network communication in pain processing and modulation. These findings may provide novel insight for understanding the mechanisms of pain chronicity in CTN patients.

SWATH-based quantitative proteomics gives insight into a significant proteome shift in UCB-1 pistachio rootstock under salt stress

The hybrid pistachio rootstock UCB-1 is especially tolerant to abiotic stresses including salinity. However, the effect of salinity on its whole proteome is obscure, which limits our ability to develop high-confident protein biomarkers to facilitate the genetic improvement of pistachio cultivars with enhanced salt-tolerance. In the present study, using sequential window acquisition of all theoretical mass spectra (SWATH-MS) technology, the expression of 1150 proteins was quantified, with the expression of 459 proteins significantly impacted by salt exposure. Functional enrichment determined that salinity stress increases the level of proteins associated with response to salt stress and reduces the level of ribosomal subunits. We used KEGG pathway enrichment analysis to link salt exposure with a set of alterations in several pathways including ribosome components, biosynthesis of phenylpropanoids, and carbon fixation in photosynthetic organisms. To track the potential consequences of protein alteration with more crucial biological functions in the enriched pathways, the incorporation of SWATH-MS data and protein-protein interactions (PPIs) analysis were then conducted. Centrality analysis of the three top-ranked enriched pathways highlighted that any alterations in the abundance of EMB3010, PA2, NAD-ME1, and PGAM proteins may result in desirable consequences on salt tolerance in UCB-1. Therefore, centrality analysis of whole proteome survey-derived responsive PPIs was useful in the identification, prioritization, and selection of potential biomarker candidates by which the need for expensive and time-consuming experimental validation will be minimized. At the early selection stages in tree breeding programs, integration of proteomics-generated resources with conventional programs can assist in developing fast selection assays.

Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

The colorectal cancer tumor microenvironment presents significant genetic heterogeneity with mutations in genes in several signaling pathways. Detecting these driver genes through wet lab experiments is costly and time-consuming. Computational models and bioinformatic tools have become a vital alternative in this effort. One of these novel computational methods, Centrality Analysis, models molecular functions, biological processes and biochemical pathways by creating and analyzing protein-protein interaction networks. Centrality Analysis is an approach to quantify node (in this case, protein) importance in these networks. Essential proteins play critical roles in cell function; therefore, centrality measures serve as a basis to study the relationship between lethality and essentiality by evaluating the topological features of the network. However, there is no established standard to determine the most appropriate centrality measure for analyzing a specific network. The choice of a suitable set is complicated by the impact of network topology because results vary based on network structure, correlation among the selected set of measures, and network data collection methods used. We hypothesized that centrality scores can be used to predict driver genes while statistical and machine learning analyses can identify the relevant centrality features for this task. We proposed different analyses to select a valid set of centrality algorithms to predict driver and non-driver genes. We first recreated a protein-protein interaction network for colorectal cancer featuring known driver and passenger genes, and then compared the centrality scores of eight different algorithms using statistical analysis. We further validated the results by implementing machine learning models. Both analyses identified betweenness and closeness centrality algorithms as most important to predict driver versus non-driver genes.

Molecular mechanism of Hedyotis Diffusae Herba in the treatment of lupus nephritis based on network pharmacology.

Aims: To determine the bioactive components of Hedyotis Diffusae Herba (HDH) and the targets in treating lupus nephritis (LN), and so as to elucidate the protective mechanism of HDH against LN. Methods and results: An aggregate of 147 drug targets and 162 LN targets were obtained from online databases, with 23 overlapped targets being determined as potential therapeutic targets of HDH against LN. Through centrality analysis, TNF, VEGFA and JUN were screened as core targets. And the bindings of TNF with stigmasterol, TNF with quercetin, and VEGFA with quercetin were further validated by molecular docking. By conducting Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses for drug targets, disease targets and the shared targets, TNF signaling pathway, Toll-like receptor signaling pathway, NF-kappa B signaling pathway and HIF-1 signaling pathway, etc., were found in all these three lists, indicating the potential mechanism of HDH in the treatment of LN. Conclusion: HDH may ameliorate the renal injury in LN by targeting multi-targets and multi-pathways, including TNF signaling pathway, NF-kappa B signaling pathway, HIF-1 signaling pathway and so on, which provided novel insights into further researches of the drug discovery in LN.

Mapping Economists’ Belief Spaces Using Survey Data

Most survey research on the beliefs of economists has focused on measuring consensus within the profession. Researchers have given less emphasis to other aspects of the organization of economists’ belief systems. This paper shows using representative survey data for the first time that economists’ beliefs on an important subset of policy-relevant beliefs are ideologically aligned, despite moderately high levels of agreement on these issues. The analysis does not support the existence of a second dimension of alignment capturing a Keynesian/anti-Keynesian split on macroeconomic stabilization topics. Going beyond conventional methods, the paper also reports the results of belief network centrality and correlational class analyses, methods motivated by recent developments in cognitive science and cultural sociology. This analysis suggests that beliefs including those relating to inequality and redistribution, the level of government spending, environmental regulation, and the minimum wage play a generative role in economists’ belief systems. The results also indicate that the main source of heterogeneity in economists' belief systems is between ideologically aligned and less ideologically aligned subgroups. There is limited evidence of qualitatively distinct patterns of construals of relations between beliefs. Finally, although the analysis is tentative, I fail to find evidence supporting the hypothesis of a decrease in ideological alignment since the 1970s.

Wildlife health surveillance: gaps, needs and opportunities.

Disease emergence represents a global threat to public health, economy and biological conservation. Most emerging zoonotic diseases have an animal origin, most commonly from wildlife. To prevent their spread and to support the implementation of control measures, disease surveillance and reporting systems are needed, and due to globalisation, these activities should be carried out at the global level. To define the main gaps affecting the performance of wildlife health surveillance and reporting systems globally, the authors analysed data from a questionnaire sent to National Focal Points of the World Organisation for Animal Health that inquired on structure and limits of wildlife surveillance and reporting systems in their territories. Responses from 103 Members, covering all areas of the globe, revealed that 54.4% have a wildlife disease surveillance programme and 66% have implemented a strategy to manage disease spread. The lack of dedicated budget affected the possibility of outbreak investigations, sample collection and diagnostic testing. Although most Members maintain records relating to wildlife mortality or morbidity events in centralised databases, data analysis and disease risk assessment are reported as priority needs. The authors' evaluation of surveillance capacity found an overall low level, with marked variability among Members that was not restricted to a specific geographical area. Increased wildlife disease surveillance globally would help in understanding and managing risks to animal and public health. Moreover, consideration of the influence of socio-economic, cultural and biodiversity aspects could improve disease surveillance under a One Health approach.

Centrality Analysis of Protein-Protein Interaction Networks Using R.

Proteins are structural and functional components of cells. They interact with each other to drive specific cellularfunctions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologicallyimportant proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein's strategic positions in the network using an R programming language.

A Study on the Designation Institution for Supercomputer Specialized Centers in Republic of Korea

In Korea, specialized centers are designated for 10 strategic fields for the purpose of jointly utilizing supercomputer resources at the national level. Based on the “National Supercomputing Innovation Strategy,” it plans to select 10 centers in three stages by 2030, and has now completed the designation of the first-stage specialized centers in 2022. With the second designation in 2024 ahead, it is urgent to review and improve the existing designation institution for fairer and more effective selection of specialized centers. Therefore, this paper analyzed the influence of evaluation items and the influence of evaluation items on evaluation results by using logistic regression analysis and network centrality analysis to prepare improvement plans for the existing evaluation model. As a result of the analysis, improvement measures were derived, such as subdividing evaluation items with low impact, expanding the items, and lowering the allotment of evaluation items with low impact.

Electric Grid Vulnerability Analysis to Identify Communities Prone to Wildfires

Natural hazards, like wildfires, present various challenges to the electric grid that can leave many communities without power. To identify vulnerabilities in the grid and the corresponding at-risk communities, this work considers the implementation of two topological assessment approaches, namely betweenness centrality and minimum cut, and combines the results from each with spatial fire probability data. The results from a betweenness centrality analysis identified at-risk communities whose critical lines, necessary for routing power to the community from the numerous generators, were found to be at-risk if they were located within high probability burn zones. Communities at-risk of separation from the grid with one cut (or electrical shorting) of a transmission line due it’s proximity to a high burn probability (BP) area were also identified using the minimum cut Graph Theory algorithm. When the methodologies were applied to a demonstration transmission grid, the results found that about about one third of the 585 substations had centrally located lines in high BP areas. About 46% of the substations require just one cut to be removed from the grid, and the average length of these one-cut segments were 37 km with the longest at 188 km.

National Petition Analysis Related to Nursing: Text Network Analysis and Topic Modeling

This study aimed to identify the main keyword, network structure, and main topics of the national petition related to "nursing" in South Korea. Data were gathered from petitions related to the national petition in Korea Blue House related to the topic "nursing" or "nurse" from August 17, 2017, to May 9, 2022. A total of 5,154 petitions were searched, and 995 were selected for the final analysis. Text network analysis and topic modeling were analyzed using the Netminer 4.5.0 program. Regarding network characteristics, a density of 0.03, an average degree of 144.483, and an average distance of 1.943 were found. Compared to results of degree centrality and betweenness centrality, keywords such as "work environment," "nursing university," "license," and "education" appeared typically in the eigenvector centrality analysis. Topic modeling derived four topics: (1) "Improving the working environment and dealing with nursing professionals," (2) "requesting investigation and punishment related to medical accidents," (3) "requiring clear role regulation and legislation of medical and nonmedical professions," and (4) "demanding improvement of healthcare-related systems and services." This is the first study to analyze Korea's national petitions in the field of nursing. This study's results confirmed both the internal needs and external demands for nurses in South Korea. Policies and laws that reflect these results should be developed.

Network analysis of publications on studies of Parkinson Disease

More than 10 million people worldwide are suffering from Parkinson Disease (PD). We analyse publications on studies of PD over the period from 2015 to 2021. We have collected about 70 thousand papers from 4912 different journals. After the data preprocessing 39811 publications and 3292 journals are left.Methods of centrality analysis have been applied to identify the most significant publications and journals in the field. Classical and new centrality indices have been evaluated for the whole period and for each year. It can be noticed that changing the parameters makes it possible to extract groups of specialized journals and fields of study, which intensively cite each other.

Visual analysis of knowledge graph based on fuzzy sets in Chinese martial arts routines

<abstract> <p>There are many schools of Chinese martial arts routines and complex movements; research on this topic is more geared toward Taijiquan (a kind of traditional Chinese shadow boxing), which is a more well-known type of martial arts. Therefore, the purpose of this paper is to visually analyze the research of Chinese martial arts routines based on the knowledge graph method and to propose a knowledge graph method based on the fuzzy set theory, which is called the transF model throughout this paper. The transF model used the fuzzy relational operation of vectors to not only reduce the computational complexity, but to also better integrate multi-dimensional data, especially when the training set is not particularly sufficient. For the visual analysis of Chinese martial arts routines, this paper selected the 16-year data from 2005 to 2020 as the analysis sample, analyzed high-yield institutions and high-yield authors, and conducted a centrality analysis of the whole dataset. From the structure of the knowledge graph, traditional martial arts are the core part of Chinese martial arts, with a centrality of 0.14. Competitive martial arts are the main branch of Chinese martial arts and the third core after Tai Chi and traditional martial arts, with a centrality of 0.41, which is higher than that of traditional martial arts. This shows its importance in martial arts research.</p> </abstract>