Closeness Centrality Research Articles

Evidence-based practice in traditional persian medicine (TPM): a stakeholder and social network analysis

BackgroundThe utilization of complementary and alternative medicine (CAM) is experiencing a global surge, accompanied by the adoption of national CAM policies in numerous countries. Traditional Persian medicine (TPM) is highly used as CAM in Iran, and the ongoing scientific evaluation of its interventions and the implementation of evidence-based medicine (EBM) encounters various barriers. Therefore, comprehending the characteristics and interactions of stakeholders is pivotal in advancing EBM within TPM policies. In this study, we utilized both classical stakeholder analysis and social network analysis to identify key stakeholders and potential communication patterns, thereby promoting EBM in TPM policy-making.MethodsA cross-sectional nationwide stakeholder analysis was conducted in 2023 using snowball sampling. The interviews were carried out using a customized version of the six building blocks of health. Data were collected through semi-structured interviews. Stakeholders were assessed based on five factors (power, interest, influence, position, and competency). The connections and structure of the network were analyzed using degree, betweenness, closeness centrality, and modularity index to detect clusters of smaller networks.ResultsAmong twenty-three identified stakeholders, the Ministry of Health and Medical Education (MOHME) and the Public were the most powerful and influential. The Iranian Academy of Medical Sciences was the most competent stakeholder. Social network analysis revealed a low density of connections among stakeholders. Pharmaceutical companies were identified as key connectors in the network, while the Public, supreme governmental bodies, and guilds acted as gatekeepers or brokers. The MOHME and Maraji were found to be high-ranking stakeholders based on four different centrality measures.ConclusionThis study identifies powerful stakeholders in the network and emphasizes the need to engage uninterested yet significant stakeholders. Recommendations include improving competence through education, strengthening international relations, and fostering stronger relationships. Engaging key connectors and gatekeepers is essential for bridging gaps in the network.

Knowledge Extraction via Machine Learning Guides a Topology‐Based Permeability Prediction Model

AbstractThe complexity and heterogeneity of pore structure present significant challenges in accurate permeability estimation. Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. The present study aims to develop a more robust and accurate permeability prediction model via knowledge extraction from ML model. We first establish an ML model between permeability and the geometry‐topology characteristics of porous media using Extreme Gradient Boosting (XGBoost) algorithm. The data set used to fit ML model is prepared from 458 samples of different types of porous media. Using the SHapley Additive exPlanations (SHAP) value, the influence of each feature on permeability prediction is quantified. It is found that the closeness centrality (topology feature), tortuosity, porosity (macroscopic features) and throat diameter, throat length, pore diameter (pore network features) are vital for permeability prediction. Guided by partial dependence calculation, the unknown function relationship between permeability and the top six important features is established. The novel permeability prediction model incorporating topology feature improves the prediction accuracy and demonstrates strong applicability across diverse data sets. This new model presents an optimal balance between simplicity and performance, rendering it a compelling alternative for permeability prediction in porous media. The research provides a novel referable framework of knowledge extraction via ML to reveal the important features and establish the potential relationship that can be extended and applied in other research fields.

Computational prediction of crucial genes involved in gonorrhea infection and neoplastic cell transformation: A multiomics approach

Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, protein‒protein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.

Establishment and evaluation of a premenstrual syndrome rat model induced by progesterone-withdrawal and emotional-stimulation

Premenstrual syndrome(PMS) lacks a highly consistent and feasible animal model that aligns with diagnostic and therapeutic standards in both traditional Chinese medicine(TCM) and western medicine, resulting in a lack of reliable experimental carriers for studying its pathogenesis and pharmacological effects. This study aims to systematically analyze the biological implications of PMS from the perspective of the "disease-syndrome-symptom" correlation and establish preparation and evaluation methods for an improved animal model of this disease. Firstly, clinical symptom gene sets related to the Qi stagnation syndromes due to liver depression and blood stasis in PMS in both modern medicine and TCM diagnostic standards were collected through GeneCards, DisGeNET, Mala-Cards, and the System of Foundational Diagnostic Association(SoFDA) database, as well as published literature. Based on the interaction information between genes, a "disease-syndrome-symptom" correlation network of PMS was established. Based on data mining results, an improved rat model of PMS was prepared by combining chronic restraint stress with the classical progesterone-withdrawal mo-del to simulate emotional depression caused by external environmental stimuli during the clinical onset process, inducing pathological damage from both physiological and emotional dimensions. The evaluation of the improved model before and after modification included open field experiment scores, organ indices, ovarian pathological changes, serum levels of estradiol(E_2), follicle-stimulating hormone/luteinizing hormone(FSH/LH), 5-hydroxytryptamine(5-HT), dopamine(DA), norepinephrine(NE), as well as coagulation parameters and hemorheology indexes. By calculating the degree, betweenness, and closeness centrality of nodes in the "disease-syndrome-symptom" correlation network, 163 core genes with topological importance were identified. Further biological function mining results indicated that core genes in PMS mainly participated in the regulation of the "nervous-endocrine-immune" system and pathways related to circulatory disorders. Mapping analysis of clinical phenotype symptom gene sets suggested significant correlations between core genes in PMS and depressive symptoms and pain symptoms caused by blood stasis. Compared with the simple progesterone withdrawal model, rats subjected to combined injections and restraint stress showed more significant abnormalities in open field experiment scores, ovarian tissue pathology, serum neurotransmitter levels of 5-HT and DA, as well as serum hormone levels of E_2 and FSH/LH. The modified modeling conditions exacerbated the pathological changes in blood rheology, coagulation function, and red blood cell morphology in model rats, confirming that the improved rat model could characterize the "nervous-endocrine-immune" system disorder and circulatory system disorders in the occurrence and progression of PMS, consistent with the clinical diagnostic and therapeutic standards of both TCM and western medicine. The establishment of the improved rat model of PMS can provide a reliable experimental carrier for elucidating the pathogenesis of PMS and discovering and evaluating therapeutic drugs. It also provides references for objectively reflecting the clinical characteristics of PMS in TCM and western medicine and precision treatment.

Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study

IntroductionFrailty has a significant impact on the overall quality of life of older cancer survivors, but the relationships among frailty symptoms are not well understood. This study aims to explore the specific associations among multidimensional symptoms of frailty among older cancer survivors by employing network analysis to provide supportive evidence for targeted interventions in the future. Materials and MethodsData were obtained by cluster sampling from three large Grade-A tertiary hospitals in Shandong Province, China, and collected through face-to-face interviews by trained investigators. We included patients who were diagnosed with a solid malignant tumor at the age of 60 years or older. Frailty indicators were measured by the Groningen Frailty Indicator (GFI) and analyzed primarily through network analysis, including network estimation, centrality, and stability analysis. The relative importance of a node in a network was tested by centrality analyses, and Spearman correlations were applied to estimate the relationships between symptom pairs (symptom score) and symptom clusters (standardized symptom score) in the symptom network. In terms of centrality, the indexes of strength, closeness, and betweenness were adopted to measure the importance of nodes. ResultsFive hundred and eight older cancer survivors were included, with an average age of 68.4 years (standard deviation [SD] = 5.4), and a higher proportion were male (n = 307[60.4%]). The prevalence of frailty among older cancer survivors was 58.9% (n = 299), with a mean GFI score of 4.46 (SD = 2.87). The strongest edge was between “dressing and undressing” and “going to the toilet” (r = 0.58). The nodes with the higher strength centrality were “going to the toilet” (rS=1.09), “walking around outside” (rS=0.97), and “part of social network” (rS=0.96); and the nodes with the higher closeness centrality were “mark physical fitness” (rC=0.005), “calm and relaxed” (rC=0.005), and “nervous or downhearted” (rC=0.005). DiscussionThis study demonstrated that older cancer survivors in China have a high prevalence of frailty, with self-care and social participation-related symptoms playing a key role in the multidimensional network of frailty symptoms. Psychological symptoms can rapidly influence other symptoms within this network. Therefore, prioritizing psychological symptoms in the assessment of older adults with cancer is essential for effective frailty management.

빅데이터를 활용한 유보통합에 대한 사회적 인식 분석

The purpose of this study is to find out the social discourse of ECEC integration using big data. Big data related to ECEC integration from 2013 to 2023 were collected using Textorm. The collected raw data were refined, and among them 50 keywords were selected as a result of frequency analysis. The results of the study are as follows. First, the social discourse of ECEC integration showed high frequency in the order of ‘teacher’, ‘child care center’, ’kindergarten’, ‘child care’, and ‘qualification’. As a result of cluster analysis, social discourse was formed centering on three core discourses such as teacher qualification acquisition, policy direction, and consultation subject. Second, major keywords such as ‘child care center’, and ‘kindergarten’ have a higher degree of centrality, while word such as ‘policy’, and ‘Korea National Open University’ have a higher closeness centrality. Third, the ego network analysis was conducted on the main keywords with higher centrality and frequency, such as ‘teacher’ and ‘qualification’. In addition, the ego network analysis was performed and visualized the main keywords and visualized. Through this study, the social discourse was investigated and implications for approach of ECEC integration were discussed.

Unraveling the Impacts of River Network Connectivity on Ecological Quality Dynamics at a Basin Scale

The ecological quality of river basins is significantly influenced by the complex network of river structures and their connectivity. This study measured the temporal and spatial variability of ecological quality, as reflected by remote sensing ecological indices (RSEI), and examined their responses to river network connectivity (RNC). In total, 8 RNC indices, including river structure of river density (Dr), water surface ratio (Wr), edge-node ratio (β), and network connectivity (γ), and node importance indices of betweenness centrality (BC), PageRank (PG_R), out_degree centrality (Out_D), and in_closeness centrality (In_C), were generated at the subbasin scale. Our results highlighted the significance of RNC in influencing both the values and variability of RSEI, and the extent of this influence varied across different time periods. Specifically, three distinct clusters can be extracted from the temporal variability of RSEI, representing wet, near-normal, and dry years. The river structure index of γ significantly influenced the spatial patterns of subbasin RSEIs, particularly in wet years (R2 = 0.554), whereas β displayed a pronounced U-shape correlation with subbasin RSEIs in dry years (R2 = 0.512). Although node importance indices did not correlate directly with subbasin RSEI levels, as the river structure indices did, they significantly positively affected temporal variability of subbasin RSEIs (EI_SD_t). Higher values of PG_R, Out_D, and In_C were associated with increased subbasin RSEI variability. Based on these correlations, we developed RNC-based RSEI and EI_SD_t models with high adjusted coefficients of determination to facilitate the assessment of ecosystem quality. This study provides essential insights into ecosystem dynamics related to river connectivity within a basin and offers valuable guidance for effective watershed management and conservation efforts aimed at enhancing ecological resilience and sustainability.

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality

In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%.

Characterizing collaborative networks for different arctic issues based on complex network analysis

International collaboration is widely accepted as an effective way to address Arctic-related issues, and these collaborative events have been frequently reported in recent years. To obtain the potential regular pattern hidden behind these events, a comprehensive methodology is proposed in this study. A database containing all the international collaborative events in terms of Arctic issues reported in the last decade was first developed, and these events were categorized into six aspects. Correspondingly, six subnetworks are developed, in which the nodes are represented by collaborative states, while the edges are denoted by collaborative relationships. These subnetworks are then characterized and compared by quantitative network parameters of total connections, network density, average path length, network transitivity, degree centrality, closeness centrality, and clustering coefficients. Finally, the performance of the individual states in each subnetwork is quantitatively analysed. The results show that Arctic science and research are the top priorities for international collaboration, and many uncertainties are involved in collaboration for Arctic resource utilization. In addition, the challenges faced by the non-Arctic states participating in Arctic issues are explained, and suggestions to promote international collaboration in terms of Arctic issues are proposed.

Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis.

Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD. We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality. No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected). Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

Cardiovascular disease risk: it is complicated, but race and ethnicity are key, a Bayesian network analysis.

Cardiovascular diseases are the leading cause of morbidity and mortality in the United States. Despite the complexity of cardiovascular disease etiology, we do not fully comprehend the interactions between non-modifiable factors (e.g., age, sex, and race) and modifiable risk factors (e.g., health behaviors and occupational exposures). We examined proximal and distal drivers of cardiovascular disease and elucidated the interactions between modifiable and non-modifiable risk factors. We used a machine learning approach on four cohorts (2005-2012) of the National Health and Nutrition Examination Survey data to examine the effects of risk factors on cardiovascular risk quantified by the Framingham Risk Score (FRS) and the Pooled Cohort Equations (PCE). We estimated a network of risk factors, computed their strength centrality, closeness, and betweenness centrality, and computed a Bayesian network embodied in a directed acyclic graph. In addition to traditional factors such as body mass index and physical activity, race and ethnicity and exposure to heavy metals are the most adjacent drivers of PCE. In addition to the factors directly affecting PCE, sleep complaints had an immediate adverse effect on FRS. Exposure to heavy metals is the link between race and ethnicity and FRS. Heavy metal exposures and race/ethnicity have similar proximal effects on cardiovascular disease risk as traditional clinical and lifestyle risk factors, such as physical activity and body mass. Our findings support the inclusion of diverse racial and ethnic groups in all cardiovascular research and the consideration of the social environment in clinical decision-making.

Quantifying the features of earthquake clusters in north-Central Iran, based on nearest-neighbor distances and network analysis

The statistical features of earthquake clusters in North-Central Iran (Tehran Region) are investigated, with the aim of quantitatively characterizing the properties of earthquake triggering and allow exploring their possible relations with the tectonic setting of the study area.The nearest-neighbor approach is used for the identification of the earthquake clusters in the space-time-energy domain. This approach permits for a data-driven identification of clusters so that, within multi-event clusters, the features of secondary and higher orders dependent events can be explored. The study is based on a revised dataset that is extracted from the catalog compiled by the Iranian Seismological Center (IRSC) for the period of 1996–2022. In order to exclude the effect of non-tectonic events, which turn out quite numerous within the study region, explosions within quarry-rich areas are removed; the identification of non-tectonic events is performed by considering the normalized ratios of daytime to nighttime events in an iterative removal procedure. According to preliminary analysis of the resulting catalog, an area is selected, within which a satisfactory completeness level is assessed for events with magnitude >2.0. Robust values of the scaling parameters, namely the b-value and the fractal dimension of epicenters, are also computed and are used to calculate the nearest-neighbor distances and to identify the earthquake clusters.The nearest-neighbor method also permits to investigate the internal structure of earthquake sequences, and to differentiate the spatial properties of seismicity according to the different topological features of the clusters structure. The obtained results allow us identifying two macro-areas, approximately separated by the 52°E meridian, which are characterized by different clustering features, namely: high complexity indexes, indicating simple (burst-like) structure of clusters, to the East; low complexity index, corresponding to complex multi-level (swarm-like) structure of clusters, to the West. The complexity measures, borrowed from network theory (i.e. the Closeness and Outdegree Centralization indexes), consistently capture the complexity of the identified clusters, and confirm that the cluster structures have distinct preferred geographic locations. The territorial heterogeneity of the examined clustering properties can be related with the spatial variability of tectonic, structural and geophysical features of the Alborz region, in good agreement with findings from the Alps-Dinarides junction (Northeastern Italy), a region also characterized by a contractional structural setting, mainly including reverse and strike-slip faulting systems, and by moderate to high seismic activity.

Network pharmacology-based strategy for screening active compounds and significant pathways of huachansu capsule on cancerous pain.

e24054 Background: Cancerous pain (CP) is a troublesome symptom which can not be adequately controlled. It is reported by several clinical studies that Huachansu can help to relieve CP. However, its mechanism is unclear. In this study, we try to use network pharmacology to explore the underlying mechanism of Huachansu capsule (a capsule form of Huachansu) in the treatment of CP. Methods: Firstly, we collected Huachansu capsule’s active compounds from the literature. And then DrugBank and SwissTargetPrediction were used to obtain the drug targets. Genecards, OMIM, Drugbank, CTD, TTD and DisGeNET were used to gather CP-related genes. Cytoscape 3.7.2 was applied to construct the PPI network. The candidate genes were screened according to betweenness centrality (BC), closeness centrality (CC), degree centrality (DC) and local average connectivity (LAC). A PPI enrichment analysis was performed by MCODE. GO and KEGG analysis were carried out by Metascape. Results: In this study, we collected 11 active compounds and 235 CP-related targets of Huachansu capsule. Go analysis showed that the main related-biological functions included immune response, cytokine-mediated signaling, hypoxia. The main related-KEGG pathways included MAPK, AMPK, mTOR, erbB, Chemokine, TNF, HIF-1, toll-like receptor, PI3K-Akt, cAMP and NF-Kappa B signaling pathway. Conclusions: This study provides a systematic view of the polypharmacological characteristics of Huachansu capsule for pain treatment. Nevertheless, it still need to be verified by in vitro or in vivo studies.

Network structure indicators predict ecological robustness in food webs

AbstractFood web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

Analisa Jejaring Sosial Terhadap Fenomena Cyberbullying Fandom K-Pop pada Sosial Media Twitter

This study examines cyberbullying among K-pop fandoms through social network analysis (SNA) using data from Twitter, a social media platform. The phenomenon of K-pop gaining global popularity also brings negative impacts, such as cyberbullying, which can affect the psychological well-being of victims. Using R Studio and Gephi analysis tools, this study applied centrality values, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, to identify influential accounts in the spread of the cyberbullying phenomenon. This analysis provides insight into the interaction and influence between Twitter user accounts in the context of cyberbullying. The main objective of this research is to paint a picture of the cyberbullying phenomenon involving various K-pop fandoms and identify the accounts that play an essential role in the related communication network.

Identification of risk key factors and prevention strategies for collision accidents between merchant and fishing vessels in China waters based on complex network

In recent years, Collision Accidents between Merchant and Fishing Vessels (CAMF) in China waters have been increasing, resulting in severe casualties and property losses. To effectively prevent such frequent collision accidents and ensure the safety of waterways and navigation routes, this study aims to identify the key risk factors associated with CAMF. Firstly, the Human Factors Analysis and Classification System (HFACS) model is employed as a comprehensive framework to extract the risk factors contributing to CAMF. By introducing edge length as the weight of network edges, a directed weighted network of CAMF is constructed. Secondly, the overall topological characteristics of the network are analyzed, and the network's robustness is studied based on the degree centrality, weighted closeness centrality, and weighted betweenness centrality of the risk factors in the network, and the key factors contributing to the accidents are identified. The research findings indicate that the network exhibits evident scale-free characteristics and small-world network properties. Deliberate attacks on approximately top 15% of the risk factors lead to a 55% degradation in the global efficiency of the accident network. Ultimately, accident prevention and control strategies and recommendations are proposed from the perspectives of both merchant and fishing vessels.

Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score.

Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (SACD), Similarity based on Average Betweenness (SACB), Similarity based on Average Closeness (SACC), and Similarity based on Average Clustering Coefficient (SACCC). Our approach involved determining centrality scores for each node, calculating the average centrality for the entire graph, and deriving similarity scores through common neighbors. We then applied centrality scores to these common neighbors and identified nodes with above average centrality. To evaluate our approach, we compared proposed measures with existing local similarity-based link prediction measures, including common neighbors, the Jaccard coefficient, Adamic-Adar, resource allocation, preferential attachment, as well as recent measures like common neighbor and the Centrality-based Parameterized Algorithm (CCPA), and keyword network link prediction (KNLP). We conducted experiments on four real-world datasets. The proposed similarity scores based on average centralities demonstrate significant improvements. We observed an average enhancement of 24% in terms of Area Under the Receiver Operating Characteristic (AUROC) compared to existing local similarity measures, and a 31% improvement over recent measures. Furthermore, we witnessed an average improvement of 49% and 51% in the Area Under Precision-Recall (AUPR) compared to existing and recent measures. Our comprehensive experiments highlight the superior performance of the proposed method.

Social Network Analysis: Political Elite Actors #DemokratLawanBegal on Twitter

This research explores the impact of the #DemokratLawanBegal hashtag on Twitter, focusing on its role in shaping political discourse and influencing party leaders' alignment. It aims to uncover relationships among elite actors and identify influential figures within the Twitter network using this hashtag. Drawing on concepts like political communication, political elites, Twitter's importance, and Social Network Analysis (SNA), the study adopts SNA methodologies with tools like Netlytic and Gephi Apps. Data collection occurred throughout 3-10 April 2023 to maintain relevance. The findings highlight the significant impact of the #DemokratLawanBegal hashtag in shaping political discourse and the power struggle within the Democratic Party. AHY emerges as a more influential figure than Moeldoko, and Twitter, particularly through this hashtag, plays a central role in political communication and framing strategies. Social Network Analysis reveals a closely interconnected network of actors, with the official Democratic Party account (@pdemokrat) central to information dissemination. Some actors facilitate swift information sharing (closeness centrality), while others serve as key connectors (betweenness centrality). These insights deepen our understanding of contemporary political communication dynamics, offering implications for political campaigns, policy advocacy, and digital strategies.

Mapping analytical methods between input–output economics and network science

AbstractThe input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis.