Degree Centrality Research Articles

Optimizing a multimodal hub and spoke network for vaccine distribution using regional vulnerability index during healthcare emergencies

Purpose A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution. Design/methodology/approach A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI. Findings Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality. Originality/value This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

Network position and academic achievement in middle school

AbstractDoes network position promote academic achievement net of the characteristics of friends in the network? Using nearly complete friendship network data at two middle schools, we estimate fixed‐effects models to examine whether degree centrality and brokerage position affect grades, net of all time‐invariant student characteristics, and the observable content of the friend network. The results show that more central network positions advance academic achievement, but these effects are not the same across mathematics and language arts subjects. Degree centrality affects mathematics grades, while brokerage position impacts performance in language arts. These findings highlight the importance of network position on academic achievement and the relevance of different types of centrality for achievement in mathematics and language arts.

Altered brain functional network connectivity and topology in type 2 diabetes mellitus

IntroductionType 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.MethodsEighty-five T2DM patients and 67 healthy controls (HCs) were included. All participants underwent clinical, neuropsychological, and laboratory tests, followed by MRI examinations, including resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional high-resolution T1-weighted imaging (3D-T1WI) on a 3.0 T MRI scanner. Post-image preprocessing, brain functional networks were constructed using the Dosenbach atlas and analyzed with the DPABI-NET toolkit through graph theory.ResultsIn T2DM patients, functional connectivity within and between the default mode network (DMN), frontal parietal network (FPN), subcortical network (SCN), ventral attention network (VAN), somatosensory network (SMN), and visual network (VN) was significantly reduced compared to HCs. Conversely, two functional connections within the VN and between the DMN and SMN were significantly increased. Global network topology analysis showed an increased shortest path length and decreased clustering coefficient, global efficiency, and local efficiency in the T2DM group. MoCA scores were negatively correlated with the shortest path length and positively correlated with global and local efficiency in the T2DM group. Node network topology analysis indicated reduced clustering coefficient, degree centrality, eigenvector centrality, and nodal efficiency in multiple nodes in the T2DM group. MoCA scores positively correlated with clustering coefficient and nodal efficiency in the bilateral precentral gyrus in the T2DM group.DiscussionThis study demonstrated significant abnormalities in connectivity and topology of large-scale brain functional networks in T2DM patients. These findings suggest that brain functional network connectivity and topology could serve as imaging biomarkers, providing insights into the underlying neuropathological processes associated with T2DM-related cognitive impairment.

Political participation and voluntary associations : A hypergraph case study.

Civic organizations, ranging from interest groups to voluntary associations, significantly influence policy formation in representative democracies. This work presents a local case study that examines the relationship between voluntary associations and local political institutions in a village with nearly two thousand residents. Traditionally, sociologists' approaches have focused on individual characteristics such as age, gender, or socio-professional status. In this study, we analyze social interactions between members of organizations modeled through a hypergraph. Specifically, we model interactions as hyperedges that correspond to activities proposed by organizations and involve the individuals who participate in those activities. Our analysis reveals a community-based structure, where members of similar types of organizations tend to interact more frequently. To quantify 'political participation,' we introduce an interaction-based measure that extends degree centrality. We also introduce the 'diversity coefficient' as an extension of degree centrality to capture an individual's ability to participate in activities composed of members from different communities. Among other centrality measures, we find that the diversity coefficient is the most significant factor in explaining political participation among members of associations.

Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods

Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model.

Analysis of Multi-Interacting Networks and Their Application to HIV Transmission Among Men Who Have Sex with Men

Network science is a powerful tool for understanding the complex interactions between individuals and is widely used to study the spread of infectious diseases. Men who have sex with men (MSM) have a high risk of HIV transmission, and sex-role preference is an essential element of HIV spread. Considering the preferences of MSM groups and the effective connections with actual transmission rates, this study established a random network (symmetric degree distribution) and a scale-free network (asymmetric degree distribution), respectively. The matrix centrality theory and computer numerical simulation are combined to analyze HIV transmission patterns in MSM groups. The results indicate that the stochasticity in the asymmetric degree distribution network is higher than in the symmetric degree distribution network. Degree and eigenvector centrality are similar in asymmetric or symmetric degree distribution networks. The centrality eigenvector can reflect more information because it includes both the node’s degree and its connections’ degrees. However, when many individuals are infected, the degree of centrality may directly come into play.

Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace

This study delves into interdisciplinary research directions in human posture recognition, covering vision-based and non-vision-based methods. Visually analyzing 3066 core research papers published from 2011 to 2024 with CiteSpace software reveals knowledge structures, research topics, key documents, trends, and institutional contributions. In-depth citation analysis identified 1200 articles and five significant research clusters. Findings show that in recent years, deep learning and sensor-based methods have dominated, significantly improving recognition accuracy, like the deep learning-based posture recognition method achieving 99.7% verification set accuracy with a 20-ms delay in a controlled environment. Logarithmic growth analysis of annual publications, supported by logistic model fitting, indicates the field’s maturation since 2011, with a shift from early simple applications of traditional and deep learning algorithms to integrating interdisciplinary approaches for problem-solving as the field matures and a predicted decline in future breakthroughs. By integrating indicators like citation bursts, degree centrality, and sigma, the research identifies interdisciplinary trends and key innovation directions, showing a transition from traditional to deep learning and multi-sensor data fusion methods. The integration of biomechanics principles with engineering technologies highlights new research paths. Overall, this study offers a systematic overview to identify gaps, trends, and innovation directions, facilitating future research and providing a roadmap for innovation in human posture recognition.

Social Network Analysis of Self-Management Behavior Among Older Adults With Diabetes.

This study explores the influence of social network structures on self-management behaviors among older adults with diabetes in rural Korean villages, focusing on dietary management, physical activity, and blood glucose monitoring. Employing social network analysis (SNA), the study assessed network structures in three villages, focusing on variations in degree, closeness, and betweenness centralities to understand their impact on health behavior dissemination and adoption. The analysis identified significant differences in network configurations across the villages. Village C, characterized by the highest closeness centrality, facilitated the efficient diffusion of health-related information, thereby promoting the rapid adoption of self-management behaviors. In contrast, Village B exhibited a prominent central node with considerable influence, underscoring its potential as a strategic focal point for targeted health interventions. Social network analysis is useful for identifying influential nodes within community structures to enhance chronic disease management strategies. By tailoring interventions to the unique attributes of a community's network, significant improvements in diabetes self-care efficacy can be achieved. This approach presents a scalable and sustainable framework for community-oriented health interventions. It provides a foundation for further research into the structural and functional dynamics of diabetes self-management networks.

Reorganization of gray matter networks in patients with Moyamoya disease

Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) (P = 0.006) and local efficiency (Eloc) (P = 0.013). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients (P < 0.001, Bonferroni corrected). MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex (P < 0.05, FDR corrected). Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum (P < 0.05, FDR corrected). Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients (P < 0.05, Bonferroni corrected). Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes.

Cyber Espionage in the Age of Artificial Intelligence: A Comparative Study of State-Sponsored Campaign

This study investigates the transformative role of artificial intelligence (AI) in state-sponsored cyber espionage, focusing on its dual use in offensive and defensive operations. Using data from the MITRE ATT&amp;CK Framework, FireEye APT Groups Database, UNSW-NB15 Intrusion Detection Dataset, and the Cyber Conflict Tracker by CFR, this research applied network graph analysis, multi-criteria decision analysis (MCDA), ensemble classification models, and Difference-in-Differences (DiD) analysis. Results revealed that AI-driven offensive techniques, phishing (degree centrality 0.85), and adaptive malware (betweenness centrality 0.81) significantly enhance operational precision and scalability. Defensively, ensemble classification models achieved up to 95.8% accuracy, highlighting AI's efficacy in intrusion detection. AI regulatory frameworks reduced misattribution rates by 20% and escalation incidents by 10%, demonstrating their critical role in mitigating geopolitical risks. The findings impress AI's transformative potential in advancing cyber operations and shaping international policy and governance. By addressing challenges such as attribution, escalation risks, and ethical dilemmas, this study highlights the necessity for stronger global cooperation and regulatory frameworks to navigate the dual-use nature of AI, providing actionable insights for policymakers, cybersecurity professionals, and researchers, emphasizing the urgency of aligning technological advancements with strategies for enhancing global cybersecurity resilience.

Abnormal sensory processing cortex in insomnia disorder: a degree centrality study.

Insomnia disorder is a significant global health concern. This research aimed to explore the pathogenesis of insomnia disorder using static and dynamic degree centrality methods at the voxel level. A total of 29 patients diagnosed with insomnia disorder and 28 healthy controls were ultimately included to examine differences in degree centrality between the two groups. Additionally, the relationship between altered degree centrality values and various clinical indicators was analyzed. The results revealed that patients with insomnia disorder exhibited higher static degree centrality in brain regions associated with sensory processing, such as the occipital gyrus, inferior temporal gyrus, and supramarginal gyrus. In contrast, lower static degree centrality was observed in the parahippocampal gyrus, amygdala, insula, and thalamus. Changes in dynamic degree centrality were identified in regions including the parahippocampal gyrus, anterior cingulum, medial superior frontal gyrus, inferior parietal gyrus, and precuneus. Notably, a negative correlation was found between dynamic degree centrality in the inferior parietal gyrus and the Pittsburgh Sleep Quality Index, while a positive correlation was observed between static degree centrality in the inferior temporal gyrus and the Hamilton Depression Scale. These findings suggest that dysfunction in centrality within the sensory processing cortex and subcortical nuclei may be associated with the sleep-wake imbalance in individuals with insomnia disorder, contributing to our understanding of hyperarousal mechanisms in insomnia. Moreover, the abnormalities observed in the default mode network and the salience network provide insights into understanding the neuropathogenesis of insomnia from both static and dynamic centrality perspectives. The clinical trial registration number: ChiCTR2200058768. Date: 2022-04-16.

The dynamics of mental health policy in Iran over the last century

BackgroundMental disorders represent a significant global health concern, accounting for a substantial proportion of disabilities worldwide. Given its significance, it has consistently been a priority for health policy makers. The aim was to provide a comprehensive assessment of mental disorder-related policy events and related interventions in Iran.MethodsA content analysis of the literature and published and unpublished official documents and local media as well as a comprehensive network analysis of the text taken from the main databases of laws and regulations and the official websites of ministries and institutions related to mental health in Iran from 1907 to 2024 were carried out. A co-occurrence network analysis and degree centrality measures were performed on the collected corpus.ResultsA total of 196 documents were identified. Following the removal of duplicate documents and content irrelevant to the topic of mental disorders, 131 documents were extracted. A substantial number of documents pertained to the years 1930–1978, which were concerned primarily with the admission and dispatch of patients to the mental health clinic and psychiatric hospitals and the financial aspects of the care and treatment of patients with mental disorders by the trustee organizations. Following the 1979 revolution, the focus shifted to the expansion of social and insurance support for these patients and their families.ConclusionsThe advancements related to severe mental disorders in Iran highlight the critical importance of public interventions in this field. Nevertheless, over the past century, no discernible pattern or coherence has been identified in this area between responsible agencies and ministries. Future policies related to mental disorders should be based on the principles of increasing community-based care, sustainable financing and economic and social support for mental disorders.

Aging disrupts the link between network centrality and functional properties of prefrontal neurons during memory-guided behavior

The prefrontal cortex (PFC) is vital for higher cognitive functions and displays neuronal heterogeneity, with neuronal activity varying significantly across individual neurons. Using calcium imaging in the medial PFC (mPFC) of mice, we investigate whether differences in degree centrality—a measure of connectivity strength within local circuits—could explain this neuronal diversity and its functional implications. In young adults, neurons with high degree centrality, inferred from resting-state activity, exhibit reliable and stable action-plan selectivity during memory-guided tasks, suggesting that connectivity strength is closely linked to functional heterogeneity. This relationship, however, deteriorates in middle-aged and older mice. A computational model simulating age-related declines in synaptic plasticity reproduces these results. In young adults, degree centrality also predicts cross-modal action-plan selectivity, but this predictive power diminishes with age. Furthermore, neurons with high action-plan selectivity are spatially clustered, a pattern that fades with aging. These findings reveal the significant aging impact on the network properties in parallel with the functional and spatial organization of the mPFC.

Neurovascular coupling dysfunction associated with inflammatory factors in sudden sensorineural hearing loss.

The neuropathologic mechanisms of sudden sensorineural hearing loss (SSNHL) are unknown. The aim of this study was to investigate the alterations of neurovascular coupling (NVC) in patients with SSNHL and its association with hematologic inflammatory factors. The amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated in 48 patients with SSNHL and 54 age-, gender-, and education-matched healthy control (HC), and also utilized the arterial spin labeling imaging (ASL) to calculate cerebral blood flow (CBF). Four indices of NVC (CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-DC) in the whole brain gray matter as well as the NVC ratio were compared between two groups. In addition, correlation analyses were performed with inflammatory factors for the NVC indexes at the global level and regional level, respectively. The NVC at global level was lower in SSNHL group than in HC, except for CBF-ALFF. At the regional level, most of the brain regions with abnormal NVC in SSNHL patients involved auditory and sensorimotor language centers and limbic system compared to HC. In addition, both at the global and regional levels, NVC metrics were shown to correlate with partial inflammatory factors or hematologic parameters, including platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), systemic immunoinflammatory index (SII), blood platelet count (PLT), and lymphocyte count (Lym). From the view of the NVC metrics, these findings provide new perspectives on the neuropathologic mechanisms and clinical treatment of SSNHL.

Evolution characteristics and invulnerability simulation analysis of global zirconium ore trade network

The burgeoning demand for zirconium, driven by the rapid development of smart devices, low-carbon energy technologies, and other emerging industries, underscores the importance of understanding the dynamics of its global trade network. However, the evolutionary patterns of the international zircon ore trade network and its resilience to disruptions remain unclear. This study constructs the international zircon ore trade network from 2013 to 2022, analyzes its structural evolution at both the network and node levels, and evaluates its robustness in 2022 using five attack strategies: random node removal, random edge removal, edge degradation, targeted removal based on node degree, and targeted removal based on node betweenness centrality. Our findings reveal that: (1) the international zircon ore trade network exhibits a shift in the import market towards Asia, with the export market dominated by Spain, the United States, and Brazil. China plays a crucial role as a bridge connecting various countries, while Japan exerts significant influence within the network; (2) the network is particularly vulnerable to targeted attacks based on node degree and betweenness centrality, highlighting the potential for significant disruption following the removal of key nodes. This study provides valuable insights for ensuring the stable and sustainable supply and consumption of zirconium resources, informing the development of targeted policies for countries and relevant industries.

Altered Intracerebellar Functional Connectivity in Friedreich’s Ataxia: A Graph-Theory Functional MRI Study

Historically, Friedreich’s Ataxia (FRDA) has been linked to a relatively preserved cerebellar cortex. Recent advances in neuroimaging have revealed altered cerebello-cerebral functional connectivity (FC), but the extent of intra-cerebellar FC changes and their impact on cognition remains unclear. This study investigates intra-cerebellar FC alterations and their cognitive implications in FRDA. In this cross-sectional, single-center study, resting-state functional MRI data from 17 patients with FRDA (average age 27.7 ± 13.6 years; F/M = 6/11) and 20 healthy controls (HC) (average age 29.4 ± 9.7 years; F/M = 9/11), all of whom underwent neuropsychological testing, were analyzed. From functional connectivity matrices, graph measures were computed at both the network and node levels using two complementary parcellations. FRDA patients exhibited decreased global efficiency (p = 0.04), nodal degree (p = 0.001) and betweenness centrality (p = 0.04) in the vermal portion of lobule VIII, along with reduced global efficiency in cerebellar regions belonging to the Control-A network (p = 0.02), one of the three subdivisions of the Frontoparietal network. Verbal memory deficits correlated with global efficiency in both the vermal portion of lobule VIII (r = 0.53, p = 0.02) and the cerebellar regions of the Control-A network (r = 0.49, p = 0.05). Graph analysis revealed regional intra-cerebellar FC changes in FRDA, marked by reduced functional centrality in cerebellar regions of the vermis and responsible for executive functions. These changes correlated with cognitive alterations, highlighting the role of the cerebellar cortex in the cognitive impairment observed in FRDA.

Bibliometric analysis of glycolysis and prostate cancer research from 2004 to 2024

BackgroundProstate cancer (PCa) ranks as the second most common disease among men and the fourth most prevalent cancer worldwide. Enhanced glycolysis and excessive lactate secretion are recognized as critical factors driving the progression of various cancers. This study systematically investigated the research trends associated with glycolysis in PCa through bibliometric analysis.MethodIn this study, we conducted a systematic search of the Web of Science and PubMed databases for literature pertaining to the glycolysis of PCa that was published between January 1, 2004, and June 30, 2024. To achieve this objective, we employed CiteSpace software to generate visualizations that illustrate countries/regions, institutions, journals, and keywords. Additionally, we extracted pertinent quantitative data. Furthermore, we utilized VOSviewer software to create a collaboration network map among various journals.ResultsBetween 2004 and 2024, a total of 408 research articles on glycolysis in PCa were published, indicating a consistent upward trend in the annual publication rate. In this field, the United States not only leads in the volume of research papers but also has the highest degree of centrality. The journal "Cancer Research" is recognized as the most influential in the field, whereas "Prostate and Cancer" serves as a significant platform for disseminating research related to glycolysis in PCa. Keyword analysis has identified four primary research directions that have dominated this field over the past two decades. The role of glycolysis and its associated enzymes in PCa underpins this research. Glycolysis has also demonstrated significant clinical value in the diagnosis and prognosis of PCa. Moreover, drugs targeting glycolytic inhibitors and natural products have exhibited therapeutic potential against this disease. By modulating glycolytic mechanisms, there is potential to increase resistance in PCa. Currently, leading research in this area encompasses the application of nanotechnology to PCa glycolysis, the roles of long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) in this metabolic pathway, and the interactions between glycolysis and other biological processes in PCa.ConclusionThis study employs bibliometric analysis to provide a comprehensive overview of research on glycolysis in PCa over the past two decades. It highlights the current state of knowledge in this field, identifies key research hotspots, and explores emerging frontiers, particularly nanotechnology, lncRNA, and miRNA, which are driving innovative research directions.

A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians.

Evolution of the collaborative innovation network in China’s integrated circuit industry: a hypernetwork analysis

ABSTRACT The integrated circuit industry is a cornerstone of modern information technology. The collaborative innovation network represents an effective path for industries to achieve technological innovation. Therefore, this paper constructs a multi-level collaborative innovation network for the integrated circuit industry and analyzes its evolutionary characteristics and formation mechanisms. The results show that: the cooperation network demonstrates an overall characteristic of ‘small clusters and large dispersions’, with prominent central nodes and a relatively high average path length, which is not conducive to information transmission. The knowledge network nodes exhibit high transmission efficiency, and the overall technological field is gradually transitioning towards focusing on technology-intensive design and innovation. The edges between heterogeneous nodes in the two-level network continue to intensify, exhibiting an overall spatial correlation characteristic of ‘multi-centers to multi-centers’. Analyzing the network formation mechanism using the Exponential Random Graph model (ERGM) reveals that nodes within the collaborative innovation network primarily establish connections through direct relationships. There is a significant interaction between cross-level connections in the two-level network. Degree centrality has a relatively prominent role in promoting network evolution. Findings here provide theoretical support and reference for the government to formulate innovation policies and optimise the industrial environment for the integrated circuit industry.

Social network analysis as a new tool to measure academic impact of physicians.

H-index is a widely used metric quantifying a researcher's productivity and impact based on an author's publications and citations. Though convenient to calculate, h-index fails to incorporate collaborations and interrelationships between physicians into its assessment of academic impact, leading to limited insight into grouped networks. We present social network analysis as a tool to measure relationships between physicians and quantify their academic impact. A bibliometric multicenter analysis was conducted on physician faculty from 129 US ACGME accredited otolaryngology programs who have publications with a physician co-author in the field. Using web searches, 2494 physician faculty were identified. Scopus IDs, h-indices, and publication data for these physicians were identified using multiple Elsevier APIs queried in December 2023. Publications with multiple otolaryngology physician co-authors were included. Network and sub network maps were generated using Gephi and analyzed with custom R scripts. Centrality measures (degree, PageRank, betweenness centralities) quantified collaboration propensity. Non-parametric correlation analysis between centrality measures and h-index was conducted. Sankey diagrams were plotted using ggplot2. A co-authorship network of 2259 physicians was constructed. Physicians were visualized as nodes with collaborations as links. Centrality measures correlated strongly with h-index (h-index vs. degree centrality: r 2 = 0.62, h-index vs. PageRank: r 2 = 0.55, h-index vs. betweenness centrality: r 2 = 0.55; p < .0001). Analysis revealed novel insights into physician network structure, identifying 14 communities primarily populated by single subspecialties with varied node density. Social network analysis showed moderate correlation between social connectedness measures and h-index, supporting its use in measuring academic impact. In otolaryngology, collaborative interactions within the academic community are strongly shaped by sub-specialty affiliation and academic institution.