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

BACKGROUND: The 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. METHODS: A 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. RESULTS: Among 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. CONCLUSION: This 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.

Determining multidimensional influences of network heterogeneity on university students' psychological and academic well-being: The mediating role of social network exhaustion.

Mobile social media has become indispensable to university students' communication with various socio-demographic populations, exposing them to diverse social networks and augmenting their network heterogeneity. Although the psychological ramifications of network heterogeneity have been extensively examined, its correlated academic outcomes remain inconclusive. The current study formulated an integrated theoretical research model based on the stressor-stress-outcome framework to investigate the influence of factors associated with network heterogeneity (specifically, privacy invasion, social comparison, self-presentation, and excessive WeChat use) on social media exhaustion, psychological well-being, and academic well-being among university students. Furthermore, the research examined the mediating effect of social network exhaustion among network heterogeneity, psychological well-being, and academic well-being. A cross-sectional survey of 1128 WeChat users revealed that social comparison and excessive WeChat use had positive associations with social network exhaustion, while privacy invasion and self-presentation were negatively correlated with social network exhaustion. Additionally, social network exhaustion was negatively correlated with psychological well-being and academic well-being. Furthermore, social network exhaustion mediated the influences of network heterogeneity on psychological well-being and academic well-being. These obtained results could contribute to a more nuanced understanding of the underlying causes of social network exhaustion and the multifaceted effects of network heterogeneity. These insights may prove valuable for practitioners to enhance university students' psychological states and academic performance in the contemporary mobile media-saturated environment.

Exploring stroke discourse on Twitter through content and network analysis among Indian users.

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

Do Adolescents Adopt the Prosocial Behaviors of the Classmates They Like? A Social Network Analysis on Prosocial Contagion.

While the influence of high-status peers on maladaptive behaviors is well-documented, socialization processes of prosocial behavior through high-status peers remain understudied. This study examined whether adolescents' prosocial behavior was influenced by the prosocial behavior of the peers they liked and whether this effect was stronger when the peers they liked were also well-liked by their classmates. Three waves of data, six months apart, were collected among Chilean early adolescents who completed peer nominations and ratings at Time 1 (n = 294, Mage = 13.29, SD = 0.62; 55.1% male), Time 2 (n = 282), and Time 3 (n = 275). Longitudinal social network analyses showed that adolescents adopted the prosocial behavior of the classmates they liked - especially if these classmates were well-liked by peers in general. In addition, adolescents low in likeability were more susceptible to this influence than adolescents high in likeability. The influence resulted both in increases and - especially - decreases in prosocial behavior, depending on the level of prosociality of the liked peer. Findings suggest that likeability represents an important aspect of peer status that may be crucial for understanding the significance of peer influence with respect to prosocial behaviors during adolescence. Pre-Registration: https://osf.io/u4pxm .

Climate security dialogues on Twitter: An annotated dataset.

Climate security refers to the risks posed by climate change on nations, societies, and individuals, including the possibility of conflicts. As an emerging field of research and public debate, where conceptual definitions are not yet fully agreed upon, gaining insights into global discussions on climate security enables systematizing its various interpretations and framings, mapping thematic priorities, and understanding information gaps that need to be filled. Considering Twitter as an important digital forum for information exchanges and dialogue, the dataset was created through the development of a query strategy based on a snowball scraping technique, which collected tweets containing hashtags related to climate security between January 2014 to May 2023. The dataset comprises 636,379 tweets. Content analysis was performed using text mining and network analysis techniques to generate additional data on sentiment, countries mentioned in the body of tweets, and hashtag co-occurrences. With almost 10 years of data, the utility of this dataset lies in the ability to assess the discursive evolution of a particular topic since its inception.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

Social integration and long-term physical and psychosocial quality of life among prostate cancer survivors in the Health Professionals Follow-up Study.

PURPOSE: Prostate cancer survivors may benefit from a supportive social environment. We investigated associations of social integration and long-term physical and psychosocial quality of life among prostate cancer survivors who were participants in the Health Professionals Follow-up Study. METHODS: We included 1,428 individuals diagnosed with non-metastatic prostate cancer between 2008 and 2016. Social integration was measured by the Berkman-Syme Social Network Index (SNI) and marital status. We fit generalized linear mixed effect models for associations of SNI and marital status with patient reported outcome measures on physical and psychosocial quality of life captured between 2008 and 2020, adjusting for age, race, employment status, body mass index, comorbidities, smoking history, and clinical factors. RESULTS: Among those with baseline SNI (N = 1,362), 46.4% were socially integrated, 20.3% were moderately integrated, 27.4% were moderately isolated, and 5.9% were socially isolated. Among those reporting baseline marital status (N = 1,428), 89.5% were married. Socially integrated survivors (vs. socially isolated) reported fewer depressive signs and better psychosocial wellbeing. Physical quality of life did not differ by social integration. Married survivors (vs. not married) reported fewer urinary symptoms, but there were no differences in bowel, sexual, or vitality/hormonal symptoms. CONCLUSIONS: Among prostate cancer survivors, being socially integrated was associated with fewer depressive signs and better psychosocial wellbeing, and married prostate cancer survivors had fewer urinary symptoms. IMPLICATIONS FOR CANCER SURVIVORS: This study highlighted aspects of long-term physical and psychosocial quality of life that are more favorable among prostate cancer survivors with a supportive social environment.

Comparison of the effect of telephone follow-up with social network follow-up program on self-efficacy and depression in patients undergoing coronary artery bypass graft surgery: A randomized controlled trial.

Introduction: Coronary artery bypass graft (CABG) surgery is a common procedure to improve blood flow to the heart muscles, but patients often face challenges during the recovery period. Self-efficacy and depression play crucial roles in patient outcomes. Telephone follow-up and social network follow-up have been introduced as interventions to enhance self-efficacy. This study aims to compare the effectiveness of telephone follow-up and social network follow-up on self-efficacy and depression in CABG patients. Method: The study is a single-blinded, randomized controlled trial conducted at Shahid Rajaee Heart Hospital in Tehran, Iran. The sample size was determined to be 99 patients who met the inclusion criteria. Data were collected using a demographic questionnaire, Sullivan's cardiac self-efficacy questionnaire, and the Beck Depression Inventory (BDI). Participants were assigned to three groups: control, telephone follow-up, and WhatsApp follow-up using randomization. Data were analyzed using IBM SPSS Statistics for Windows, version 25 (IBM Corp., Armonk, N.Y., USA). Results: The results revealed significant improvements in self-efficacy and reductions in depression scores for both the telephone and WhatsApp follow-up groups compared to the control group following the intervention (p < 0.001). Additionally, the mean self-efficacy score was higher and the mean depression score was lower in the WhatsApp follow-up group than in the telephone follow-up group after the intervention (p < 0.001). Discussion: The findings provide valuable insights for healthcare professionals in choosing appropriate interventions to enhance patients' self-efficacy levels and improve mental health outcomes. Both telephone follow-up and social network follow-up interventions have their own advantages and can be effective in supporting patients' recovery after CABG surgery.

Spatial network analysis of green electricity efficiency dynamics in the Yellow River Basin cities.

Improving the green electricity efficiency (GEE), is an important issue for China's high-quality economic development. This study presents a spatial correlation network of urban GEE in the Yellow River Basin from 2012 to 2021, constructed using an improved gravity model. Social network analysis and the quadratic assignment procedure method are employed to analyze the spatial correlation characteristics and influencing factors. The findings indicate that urban GEE in the Yellow River Basin exhibits complex and stable network characteristics. The spatial network analysis reveals that Jiayuguan City, Dongying City, Dingxi City, Zibo City, and Shizuishan City occupy central positions within the network. The results indicate that spatial adjacency, GDP per capita, industrial structure, and the level of science and technology expenditure are positively related to urban GEE, while environmental regulation and average temperature are negatively related. The findings of the study have led to policy recommendations aimed at enhancing urban GEE in the Yellow River Basin.

Dataset on global trade networks of COVID-19 medical products.

This paper presents a comprehensive dataset on the global trade dynamics of COVID-19-related medical products for the years 2019 and 2020. The dataset, derived from the BACI database, focuses on eight distinct product categories identified by six-digit codes. The trade flow data for 224 countries is structured as a multilevel network, with countries as nodes and product categories as layers. Directed edges represent trading activities, and edge weights are determined by the difference in exported values between 2019 and 2020. The dataset is provided in an edges-and-nodes format. Additionally, the associated R script transforms the data into the MuxViz R package format, facilitating further analysis and visualization of the dataset. The dataset is valuable for researchers in the field of foreign trade or medical products, and for decision-makers in these fields, whether at company or national level.

The influence of service performance in China's sci-tech commissioner system: Using social network analysis and interpretable machine learning.

The Sci-Tech Commissioner System (SCS) is a result of exploratory efforts by the Chinese government to use science and technology to strengthen the agricultural sector. Social network analysis (SNA) and machine learning (ML) techniques make it feasible to assess the service performance in China's SCS by using indicators such as group types and structure features. In this study, SNA and a clustering algorithm were employed to categorize service group types of sci-tech commissioners. By comparing the accuracy of different classification algorithms in predicting the clustering results, LightGBM algorithm was finally select to determine the clustering features of sci-tech commissioners and establish an interpretable ML model. Then, the SHAP was used to algorithm to analyze influences affecting service performance. Results show that the service forms of sci-tech commissioners are group-oriented, and that group types include small groups of young commissioners with close cooperation, larger groups of young and middle-aged commissioners, small groups of middle-aged and old commissioners with close cooperation, and isolated points of highly-influential commissioners. Furthermore, while group size is not the determinant of a commissioner's average performance, group structure and coordination ability were found to be more critical. Moreover, while differences in distinct types of service performance are caused by various factors, but good group structures and extensive social contacts are essential for high service performance.

[Spatio-temporal Differentiation of County Carbon Budget and Territorial Space Optimization Zoning Strategy in Gansu Province from the Perspective of Carbon Neutrality].

Under the background of comprehensively practicing the overall system concept of the "living community" in the new era, incorporating the carbon neutral development goal into the territorial spatial planning and construction and establishing the territorial spatial pattern and optimization strategy in line with the actual development of Gansu Province are of great significance for promoting the comprehensive green low-carbon transformation and high-quality development of regional economy and society. Taking counties in Gansu Province as an example, based on the perspective of carbon neutrality research, the land use carbon budget of 87 counties in Gansu Province in 2010, 2015, and 2021 was calculated and analyzed. GIS spatial analysis and social network analysis were used to further explore their spatial differentiation characteristics and the overall characteristics of the carbon emission spatial correlation network. At last, combined with the main function zoning, the low-carbon oriented land space optimization zoning was carried out, and differentiated low-carbon development strategies were proposed. The results were as follows： ① Carbon emissions in Gansu Province showed an upward trend, but the increase rate decreased, showing a spatial distribution of "high in the central and eastern part of the country, low in the southwest." Construction land was the main carbon source. The carbon uptake showed a spatial distribution of "high in the south and low in the north, high in the west and low in the east." Woodlands were the main carbon sinks. The net carbon emissions showed an increasing trend, and approximately 58.62% of the counties in the province were in a carbon imbalance situation. ② In 2021, the spatial network of county carbon emissions was closely related, showing a "core-edge" pattern. The Chenguan District and Qilihe District were in the core position of the network and received more correlation relationships in the network. The network contacts in Longzhong area were frequent, followed by the contacts in Longdongnan area. ③ Based on carbon emissions, carbon sequestration, and ecological carrying capacity coefficients and using the results of spatial correlation of social networks as role positions, the province was divided into four carbon-neutral sub-districts. At the same time, superimposed analysis of the main function zoning, the county area of the province was reconstructed into seven territorial space zones, and the differentiated regional low-carbon optimization development strategy was proposed for each zone.

Positive correlation between working memory and face-to-face social interaction in everyday life.

Working memory (WM) plays a crucial role in various cognitive tasks from language comprehension to problem-solving. However, its influence on social activities has remained largely unexplored. The current two studies on individual differences, a pilot (N = 329) and a pre-registered direct replication (N = 338) study, investigated the relationship between WM and outside-the-lab social interaction by using a listening span task and three social network questionnaires (e.g., how many people a participant had contacted in the past month). The consistent patterns in the two studies were (a) WM recall was positively correlated with social network size, (b) WM recall remained positively correlated with social network size even when accounting for online interactions on WhatsApp and Facebook, and (c) WM recall was positively correlated with social network size by face-to-face interaction. These novel findings would suggest connections between WM and face-to-face social interaction. It was, however, acknowledged that the obtained effect sizes were small, and that further investigation is indeed necessary. In light of this, we also clarify future directions for understanding the relationship between WM and social interaction.

Effectiveness of online social networking interventions on social isolation and quality of life of people with psychosis: A systematic review.

BACKGROUND: Social isolation is frequent in people with psychosis, contributing to negative health outcomes. Interventions including online social networking (OSN) may overcome some psychosis-related barriers and facilitate social interactions. However, evidence is currently sparse and needs to be collated in a systematic review to better understand effectiveness. METHOD: Following PRISMA guidelines, this review yielded 9835 results. Eleven publications, reporting data from five RCTs and six non-controlled studies, met the inclusion criteria. Two independent reviewers undertook data extraction and quality assessment, with results narratively synthesised. RESULTS: This review looked broadly at interventions including either purpose-build platforms for peer-to-peer interactions or existing OSN tools. Yet, we only identified interventions utilising purpose-designed platforms. Early small-scale studies suggested OSN interventions reduced social isolation, but larger effectiveness studies did not confirm these effects. No improvements in quality-of-life outcomes were identified. CONCLUSION: Higher quality and longer-term studies did not support effectiveness of current OSN interventions in reducing social isolation or improving quality of life of people with psychosis. These interventions used purpose-built platforms and encouraged OSN between selected individuals, which may explain these outcomes. Future research may explore promoting safe use of mainstream OSN platforms to expand the social networks of individuals with psychosis.

Deep network embedding with dimension selection.

Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-based, with the embedding dimension determined in a heuristic or ad hoc way, which can cause potential bias in downstream statistical inference. Additionally, existing deep embedding methods can suffer from a nonidentifiability issue due to the universal approximation power of deep neural networks. We address these issues within a rigorous statistical framework. We treat the embedding vectors as missing data, reconstruct the network features using a sparse decoder, and simultaneously impute the embedding vectors and train the sparse decoder using an adaptive stochastic gradient Markov chain Monte Carlo (MCMC) algorithm. Under mild conditions, we show that the sparse decoder provides a parsimonious mapping from the embedding space to network features, enabling effective selection of the embedding dimension and overcoming the nonidentifiability issue encountered by existing deep embedding methods. Furthermore, we show that the embedding vectors converge weakly to a desired posterior distribution in the 2-Wasserstein distance, addressing the potential bias issue experienced by existing embedding methods. This work lays down the first theoretical foundation for network embedding within the framework of missing data imputation.

Potential impact of curative and preventive interventions toward hepatitis C elimination in people who inject drugs-A network modeling study.

BACKGROUND: Injection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear. METHODS: We developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter 'degree-based'). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics. RESULTS: Combining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree. CONCLUSION: DAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

Connected interactions: enriching food web research by spatial and social interactions.

This theme issue features 18 papers exploring ecological interactions, encompassing metabolic, social, and spatial connections alongside traditional trophic networks. This integration enriches food web research, offering insights into ecological dynamics. By examining links across organisms, populations, and ecosystems, a hierarchical approach emerges, connecting horizontal effects within organizational levels vertically across biological organization levels. The inclusion of interactions involving humans is a key focus, highlighting the need for their integration into ecology given the complex interactions between human activities and ecological systems in the Anthropocene. The comprehensive exploration in this theme issue sheds light on the interconnectedness of ecological systems and the importance of considering diverse interactions in understanding ecosystem dynamics. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Issues Related to the Use of Visual Social Networks and Perceived Usefulness of Social Media Literacy During the Recovery Phase: Qualitative Research Among Girls With Eating Disorders.

BACKGROUND: The patient-centered approach is essential for quality health care and patient safety. Understanding the service user's perspective on the factors maintaining the health problem is crucial for successful treatment, especially for patients who do not recognize their condition as clinically relevant or concerning. Despite the association between intensive use of visual social media and body dissatisfaction and eating disorders, little is known about the meanings users assign to posting or searching for edited photos and the strategies they use to protect themselves from digital risks. OBJECTIVE: This study aims to examine how young women recovering from eating disorders in Northern Italy perceive the health risks and potential benefits associated with visual social networks (ie, Instagram and Snapchat). The literature has found these platforms to be detrimental to online body comparisons. It also explores the perceived usefulness, willingness, and personal interest in coconstructing social media literacy programs with girls recovering from eating disorders. METHODS: A total of 30 semistructured interviews were conducted with adolescent girls aged 14-17 years at the end of their treatment for eating disorders. The following areas of research were addressed: (1) the meanings associated with the use of Instagram and Snapchat; (2) the investment in the photographic dimension and feedback; (3) the impact of visual social networks on body experiences; (4) the potential and risks perceived in their use; (5) the importance of supporting girls undergoing treatment for eating disorders in using social networks; and (6) the usefulness and willingness to co-design social network literacy programs. Content analysis was applied. RESULTS: A total of 7 main contents emerged: active or passive role in using social networks, the impact of online interactions on body image, investment in the photographic dimension, effects on self-representation, perceived risks, self-protective strategies, and potential benefits. The findings highlight a strong awareness of the processes that trigger body comparisons in the virtual context, creating insecurity and worsening the relationship with oneself. The self-protective behaviors identified are the development of critical thinking, the avoidance of sensitive content, increased control over social networking site use, and a certain skepticism toward developing antagonistic ideologies. All these topics were considered fundamental. CONCLUSIONS: The findings provide important insights for health professionals working with youth in preparing media literacy programs. These programs aim to reduce potential risks and amplify the positive effects of online resources. They underscore the importance of addressing this issue during hospitalization to develop skills and critical thinking aimed at changing small habits that perpetuate the problem in everyday life. The inherent limitations in current service practices, which may not adequately address individual needs or impact posttreatment life, must also be considered.

Prediction of influential nodes in social networks based on local communities and users' reaction information.

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users' social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Applying Social Network Theory to Vaping in High School: Implications for Person-Centered Intervention.

BACKGROUND: Peer influence on risky behavior is particularly potent in adolescence and varies by gender. Smoking prevention programs focused on peer-group leaders have shown great promise, and a social influence model has proven effective in understanding adult smoking networks but has not been applied to adolescent vaping until 2023. This work aims to apply a social influence model to analyze vaping by gender in a high school network. METHODS: A high school's student body was emailed an online survey asking for gender, age, grade level, vape status, and the names of three friends. Custom Java and MATLAB scripts were written to create a directed graph, compute centrality measures, and perform Fisher's exact tests to compare centrality measures by demographic variables and vape status. RESULTS: Of 192 students in the school, 102 students responded. Students who vape were in closer-knit friend groups than students who do not vape (p < .05). Compared to males who vape, females who vape had more social ties to other students who vape, exhibiting greater homophily (p < .01). Compared to females who do not vape, females who vape were in closer-knit friend groups (p < .05) and had more ties to other students who vape (p < .01). CONCLUSION: Differences in vaping by social connectedness and gender necessitate school and state policies incorporating the social aspect of vaping in public health initiatives. Large-scale research should determine if trends can be generalized across student bodies, and more granular studies should investigate differences in motivations and social influence by demographic variables to individualize cessation strategies.