Centrality Scores Research Articles

Mapping the Mpox discourse: A network and sentiment analysis

Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.

Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach

BackgroundAlzheimer’s Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.MethodsOur study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.ResultsThe results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions’ activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.ConclusionsOur findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

Node Centrality Inference via Hypothesis Testing

ABSTRACTCentral nodes, also known as vital or critical nodes, play a unique role in networks of various domains. To date, numerous centrality evaluation methods have been proposed to identify central nodes from different types of networks. However, most of existing methods did not address this issue from a rigorous significance testing perspective. As a result, it is hard for us to judge whether an identified central node is true or not and control the quality of reported nodes in a statistically sound manner. In this paper, we propose a centrality evaluation method based on hypothesis testing, where the test statistic is the degree sum of the neighbors of the node. We further derive an analytical p value and its upper bound under the Erdös–Rényi model, which can be employed as the centrality score for vital node recognition. To demonstrate the effectiveness of our method, extensive experiments are conducted on both simulated and real networks. The experimental results show that (1) our testing‐based centrality measure in terms of p value is capable of distinguishing central nodes from non‐central ones effectively and (2) our method is able to identify central nodes more accurately than those classic centrality measures and state‐of‐the‐art (SOTA) centrality evaluation methods.

Unveiling the Molecular Mechanisms of Glioblastoma through an Integrated Network-Based Approach.

Background/Objectives: Despite current treatments extending the lifespan of Glioblastoma (GBM) patients, the average survival time is around 15-18 months, underscoring the fatality of GBM. This study aims to investigate the impact of sample heterogeneity on gene expression in GBM, identify key metabolic pathways and gene modules, and explore potential therapeutic targets. Methods: In this study, we analysed GBM transcriptome data derived from The Cancer Genome Atlas (TCGA) using genome-scale metabolic models (GEMs) and co-expression networks. We examine transcriptome data incorporating tumour purity scores (TPSs), allowing us to assess the impact of sample heterogeneity on gene expression profiles. We analysed the metabolic profile of GBM by generating condition-specific GEMs based on the TPS group. Results: Our findings revealed that over 90% of genes showing brain and glioma specificity in RNA expression demonstrate a high positive correlation, underscoring their expression is dominated by glioma cells. Conversely, negatively correlated genes are strongly associated with immune responses, indicating a complex interaction between glioma and immune pathways and non-tumorigenic cell dominance on gene expression. TPS-based metabolic profile analysis was supported by reporter metabolite analysis, highlighting several metabolic pathways, including arachidonic acid, kynurenine and NAD pathway. Through co-expression network analysis, we identified modules that significantly overlap with TPS-correlated genes. Notably, SOX11 and GSX1 are upregulated in High TPS, show a high correlation with TPS, and emerged as promising therapeutic targets. Additionally, NCAM1 exhibits a high centrality score within the co-expression module, which shows a positive correlation with TPS. Moreover, LILRB4, an immune-related gene expressed in the brain, showed a negative correlation and upregulated in Low TPS, highlighting the importance of modulating immune responses in the GBM mechanism. Conclusions: Our study uncovers sample heterogeneity's impact on gene expression and the molecular mechanisms driving GBM, and it identifies potential therapeutic targets for developing effective treatments for GBM patients.

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

Development and Psychometric Evaluation of the Educational Leadership Scale for Faculty Members: Insights From the Network Analysis Perspective

ABSTRACTThis study aimed to develop and evaluate the psychometric properties of the Educational Leadership Scale for Faculty Members (ELSF) in Arabic. The scale was rigorously tested using both traditional methods—exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)—as well as advanced network techniques, including exploratory graph analysis (EGA) and centrality measures. The study sample consisted of 350 faculty members (56% male, 44% female) from various universities, with a mean age of 42.3 years (SD = 7.8). Participants represented a range of academic disciplines, and all were involved in educational leadership roles within their respective institutions. EFA revealed a five‐factor structure that explained 68.4% of the total variance. CFA further confirmed the model's adequacy, with fit indices showing a good model fit (CFI = 0.95, TLI = 0.94, RMSEA = 0.06). The ELSF, comprising 22 items across these five dimensions, demonstrated strong internal consistency, with a Cronbach's alpha coefficient of 0.89. Additionally, EGA supported the factor structure identified by traditional methods, reinforcing the stability and consistency of the scale. Centrality measures highlighted Item 18 as the most influential, with betweenness, closeness and strength centrality scores of 0.45, 0.78 and 0.62, respectively, within the ‘Research‐Ethical Leadership’ subscale. These findings affirm the ELSF's credibility as a robust tool for assessing educational leadership among faculty members. The scale's strong psychometric properties suggest it is well‐suited for application in diverse academic settings, including potential use across Arabic‐speaking countries such as Saudi Arabia, Qatar, the United Arab Emirates, Egypt, Oman and Kuwait.

A Social Network Analysis on Abu Sayyaf Kidnappings

The Abu Sayyaf Group (ASG) is a Philippine-based extremist organization globally known for engaging in terrorism, kidnappings, and various criminal activities. This study sought to comprehensively analyze Abu Sayyaf ’s kidnapping incidents using social network analysis to better understand their operational structures, patterns, and dynamics. The researchers applied centrality measurements to assess the significance of nodes in the network, along with the GirvanNewman algorithm for community detection to identify groups sharing similar characteristics and extract groups for various purposes. The findings revealed one kidnapping node to have the highest centrality score and holding the most significant influence and numerous incoming connections within the network. Another kidnapper node in the analysis of out-degree centrality displayed a proactive role, initiating multiple connections and shaping the network’s dynamic. The community detection uncovered seven distinct communities within the network, each demonstrating unique patterns and characteristics. The findings imply that kidnappings were conducted through groups rather than individuals. The majority of networks involving ASG members participating in multiple kidnapping events hold significant implications for national security strategies.

Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities

Among the properties describing the behavior of centrality measures with respect to network modifications, score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target with respect to the remaining nodes. It is known (Boldi and Vigna Intern Math 10:222–262, 2014, Boldi et al. Netw Sci 5(4):529–550, 2017) that score and rank monotonicity hold in directed graphs for almost all the classical centrality measures. In undirected graphs one expects that the corresponding properties hold when adding a new edge—in this case, both endpoints of the new edge should enjoy the increase in score/rank. However, recent results (Boldi et al. in Netw Sci 11(3):1–23, 2023) have shown that this is not true: for many centrality measures, it is possible to find situations in which adding an edge reduces the rank of one of its two endpoints. In this paper we introduce a weaker property for undirected networks, semi-monotonicity, in which just one of the two endpoints of a new edge is required to enjoy score or rank monotonicity. We show that this property is satisfied by closeness centrality, by a large class of distance-based centralities, and (somehow surprisingly) by betweenness centrality. In the last two cases, we prove in fact a stronger property, basin dominance, which is of independent interest.

Centrality of Information Sources in Agricultural Input Networks: Insights from Social Network Analysis

Input dealers being credible localite sources of information for farmers have huge potential of aiding faster diffusion of innovations. Under this context, understanding how the input dealers’ access and exchange information is essential for enriching them with updated information and improving the agricultural value chain. This study employs Social Network Analysis (SNA) to explore the centrality measures—degree centrality, closeness centrality, and betweenness centrality—of various information sources utilized by input dealers in Nagar Kurnool and Wanaparthy districts of Telangana during 2020. The analysis reveals that traditional sources, such as extension agents and other input dealers, are highly central to the network, with both scoring the highest in degree and closeness centrality (0.733 and 0.698, respectively). These sources are not only frequently accessed but also serve as key bridges within the network, facilitating the flow of information. Digital platforms like smartphones show growing importance, with significant degree centrality (0.667), yet they play a less critical bridging role. In contrast, social media and print media exhibit lower centrality scores, indicating their peripheral influence. The findings suggest that while traditional information sources remain dominant, digital platforms are emerging as valuable tools for information dissemination highlighting the need for strategies that strengthen traditional channels while integrating digital tools to enhance the efficiency and reach of agricultural information networks. This research contributes to a deeper understanding of the complex information ecosystem in agriculture, offering insights for policymakers and stakeholders aiming to optimize information flows within the sector.

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Electroencephalography (EEG) is commonly used to measure brain activity in clinical research. However, the abundance of channels in EEG data poses several challenges, including increased computational complexity, noise interference, and decreased efficiency in data analysis. A new graph-based method called G-EEGCS has been developed to address these issues for EEG channel selection. The graph-based optimum EEG channel selection (G-EEGCS) method constructs a directed network by establishing channel connections based on their pairwise correlations. Statistical features are used to determine similarity, and an adjacency matrix is created to represent the connectivity between the EEG channels. The influence of each channel on information flow within the network is assessed using the centrality measures. By calculating the shortest paths between all channel pairs, the algorithm quantifies the probability of each channel serving as a bridge between different parts of the graph. Channels with high centrality scores, indicating their significance in the information flow, are given priority during the selection process. An adaptive threshold is applied to optimize channel selection, ensuring that only channels that exceed the threshold, exhibit specific characteristics, and align with the overall network structure are retained. This adaptive thresholding mechanism enhances the robustness and flexibility of the G-EEGCS method, enabling personalized channel selection tailored to individual EEG datasets. The G-EEGCS approach offers a promising solution for EEG channel selection, improving the interpretability and efficiency of EEG-based studies and applications for researchers. It achieved an average accuracy of 0.9125, outperforming state-of-the-art methods. This method provides a valuable means of addressing the challenges posed by the multitude of channels in EEG data, ultimately contributing to more effective and insightful analysis in clinical research.

Resting-State Brain Function Alteration in Colorectal Cancer Patients.

To investigate the abnormal pattern of altered functional activity in the brain and the neuroimaging mechanisms underlying the cognitive impairment of patients with colorectal cancer (CRC) via resting-state functional magnetic resonance imaging (rs-fMRI). CRC patients (n = 56) and healthy controls (HCs) (n = 50) were studied. The participants underwent rs-fMRI scans and the Montreal Cognitive Assessment (MoCA). The amplitude of low-frequency fluctuations (ALFF), degree centrality (DC), regional homogeneity (ReHo), and MoCA scores, were calculated for participants. The scores of executives, visuospatial, memory, language and attention were lower in CRC patients. ReHo and ALFF values in the left postcentral gyrus, ReHo values in the right postcentral gyrus, ALFF and DC values in the left middle occipital gyrus, ReHo and DC values in the right lingual gyrus, DC values in the right angular gyrus and precuneus, and ALFF values in the left middle temporal gyrus decreased conspicuously in the CRC patients. CRC patients have abnormal resting state function, mainly in the brain areas involved in cognitive function. The overlapping brain regions with abnormal functional indicators are in the middle occipital gyrus, postcentral gyrus, and lingual gyrus. This study reveals the potential biological pathways involved in brain impairment and neurocognitive decline in patients with CRC.

Analysis of smallholder farmers' dynamics of knowledge sharing, skill transfer, and participation in using biogas (application of social network analysis)

Air pollution, population growth, and the lack of fossil energy sources are critical factors that drive people toward clean and renewable energies in the current century. Biogas is suitable for rural farmers as a source of clean energy. Since there are many social interactions in knowledge exchange and sharing, the transfer of skills, and participation among individuals, various actors are involved in the network. The power and influence of actors and the social interactions between smallholder farmers and these actors fuel the development of biogas consumption in rural areas. Therefore, this study analyzes the social network structures and characteristics of various actors by social network analysis (SNA) in knowledge sharing, the transfer of skills, and participation networks among traditional smallholder farmers in Iran. The research sample consisted of some smallholder farmers in four provinces of Fars, Khorasan Razavi, Golestan, and Kermanshah (144 smallholder farmers). The research instrument was a questionnaire designed as a matrix. The findings showed that based on the betweenness centrality score, biogas-consuming smallholder farmers (34.56) and cooperatives of smallholder farmers (24.79) were the main actors in the network of knowledge sharing, respectively. Furthermore, biogas-consuming smallholder farmers (28.04) and pioneer smallholder farmers (27.11) were the main actors in the network of participation, respectively. The betweenness centrality score revealed that cooperatives of smallholder farmers (29.23) and opinion leaders (23.33) were the main actors in the network of skills transfer, respectively. It can be concluded that these actors had high social power and influence among smallholder farmers and can be the core of the smallholder farmers’ social networks. It means that they can influence the development of biogas consumption among Iranian smallholder farmers.

Temporal dynamics of microbial communities in the water of polyculture pond system for Chinese swimming crab Portunus trituberculatus

Microorganisms as the vital component of aquaculture ecosystems, were involved in nutrient cycling, water quality regulation, and the health of cultured species. In this study, aquatic water samples were collected monthly to reveal microbial community compositions, functions, and interactions in different habitats (pond and waterway) during the culture stages of the Chinese swimming crab Portunus trituberculatus in Shandong Province. Due to the semi-closed conditions with limited water exchange, the α diversities were lower in the polyculture pond. The relative abundances of the dominant phyla varied dramatically, and most biomarkers were identified in the polyculture pond. The β diversity of the microbial communities significantly differed among culture stages and habitats. Furthermore, the heterogeneity of prokaryotic microorganisms was greater than that of eukaryotic microorganisms. In the polyculture pond, the microbial community was sensitive to nutrient cycling, with abundant functions related to “photoautotrophy”, “nitrogen_respiration” and “nitrate_respiration”, corresponding to P. trituberculatus culture activities. The co-occurrence network of the microbial community in the polyculture pond tended to be looser than that in the waterway, with a lower number of taxa and correlations. In addition, these networks had nonrandom patterns and formed “small world” topological structures. The low-abundance genera with a higher level of betweenness centrality scores were the hubs or connectors in the networks and might play an essential role in microbial community composition. Furthermore, potential pathogens (Vibrio, Acholeplasma, Photobacterium, and Flavobacterium) deserve more attention during P. trituberculatus culture. This study will be useful for understanding microbial communities in polyculture pond systems and will provide a reference for the health aquaculture of P. trituberculatus.

The Relationship between Gender Identity and Gender Centrality among Transgender, Cisgender, Nonbinary, and Intersex Individuals

ABSTRACT This study highlights the relationship between gender identity and gender centrality, including self-reported measures of the centrality of masculinity and femininity in individuals’ interactional expression, physical expression, interests, and feeling masculine or feminine. This is a secondary data analysis of a larger study (The 2019 Pleasure Study). In this analysis, it was found that there is a notable relationship between gender identity and levels of gender centrality. Transgender men and transgender women reported higher levels of gender identity centrality (“How important is your gender identity to the way you think about yourself?”) than cisgender men and women. Nonbinary people and intersex individuals reported higher levels of gender identity centrality than cisgender men and cisgender women, but lower levels than transgender men and transgender women. In an average of centrality measures (“How important are how masculine/feminine your physical expression, interactional expression, feelings, and interests are?”), trans women had the highest average centrality scores while cis men had the lowest.

Current research status and future prospects of NLRP3 inflammasome in cardiovascular diseases: a bibliometric and visualization analysis.

Cardiovascular disease (CVD) is a leading cause of global mortality, with atherosclerosis (AS) contributing to its pathological basis. Inflammation plays a critical role in the pathophysiological process of AS, and the NOD-like receptor protein 3 (NLRP3) inflammasome has been extensively studied in this context. This study aimed to analyze the research status of the NLRP3 inflammasome in cardiovascular disease and provide research directions for further exploration in this field. Using the "Bibliometrix" and "CiteSpace" software, a total of 516 articles were retrieved from the Web of Science (WoS) database published between 2012 and 2023. The search query used the keywords "["CVD" OR "cardiovascular disease"] AND ["NLRP3 inflammasome "OR "NLRP3"]". Visual analysis was performed on authors, countries, institutions, journal sources, keywords, references, and future trends. A total of 516 English articles were retrieved, showing an overall upward trend in annual publication volume with slight fluctuations. China, the United States, and Europe were the countries and regions with the highest number of published articles. Among them, China had the highest article count (170), while the United States had the highest citation count (18,664), centrality score (0.43), and h-index (90), indicating its influential role in this research area. These countries also possessed elite institutions, professional researchers, and high-impact journals, making them leading contributors in this field. The main pathogenic mechanisms of the NLRP3 inflammasome in CVD were identified as "oxidative stress", "pyroptosis", and "inflammation". The most frequently studied signaling pathways included "NF-κB", "IL-1", and "C-reactive protein". The most studied disease types were coronary heart disease, atherosclerosis, metabolic syndrome, and myocardial infarction. Additionally, research on the correlation between cholesterol markers and inflammatory indicators associated with NLRP3 inflammasome in CVD risk assessment has gained significant momentum, with the main mechanism being NLRP3/IL-6/hs-CRP and cholesterol lipoproteins emerging as a major keyword in this context. This study provides valuable insights into the research hotspots and emerging trends of the NLRP3 inflammasome in cardiovascular disease. The findings offer guidance for researchers and scholars in this field and facilitate the exploration of new research directions.

Dysregulation of long non-coding RNA gene expression pathways in monocytes of type 2 diabetes patients with cardiovascular disease

BackgroundMonocytes play a central role in the pathophysiology of cardiovascular complications in type 2 diabetes (T2D) patients through different mechanisms. We investigated diabetes-induced changes in lncRNA genes from T2D patients with cardiovascular disease (CVD), long-duration diabetes, and poor glycemic control.MethodsWe performed paired-end RNA sequencing of monocytes from 37 non-diabetes controls and 120 patients with T2D, of whom 86 had either macro or microvascular disease or both. Monocytes were sorted from peripheral blood using flow cytometry; their RNA was purified and sequenced. Alignments and gene counts were obtained with STAR to reference GRCh38 using Gencode (v41) annotations followed by batch correction with CombatSeq. Differential expression analysis was performed with EdgeR and pathway analysis with IPA software focusing on differentially expressed genes (DEGs) with a p-value < 0.05. Additionally, differential co-expression analysis was done with csdR to identify lncRNAs highly associated with diabetes-related expression networks with network centrality scores computed with Igraph and network visualization with Cytoscape.ResultsComparing T2D vs. non-T2D, we found two significantly upregulated lncRNAs (ENSG00000287255, FDR = 0.017 and ENSG00000289424, FDR = 0.048) and one significantly downregulated lncRNA (ENSG00000276603, FDR = 0.017). Pathway analysis on DEGs revealed networks affecting cellular movement, growth, and development. Co-expression analysis revealed ENSG00000225822 (UBXN7-AS1) as the highest-scoring diabetes network-associated lncRNA. Analysis within T2D patients and CVD revealed one lncRNA upregulated in monocytes from patients with microvascular disease without clinically documented macrovascular disease. (ENSG00000261654, FDR = 0.046). Pathway analysis revealed DEGs involved in networks affecting metabolic and cardiovascular pathologies. Co-expression analysis identified lncRNAs strongly associated with diabetes networks, including ENSG0000028654, ENSG00000261326 (LINC01355), ENSG00000260135 (MMP2-AS1), ENSG00000262097, and ENSG00000241560 (ZBTB20-AS1) when we combined the results from all patients with CVD. Similarly, we identified from co-expression analysis of diabetes patients with a duration ≥ 10 years vs. <10 years two lncRNAs: ENSG00000269019 (HOMER3-AS10) and ENSG00000212719 (LINC02693). The comparison of patients with good vs. poor glycemic control also identified two lncRNAs: ENSG00000245164 (LINC00861) and ENSG00000286313.ConclusionWe identified dysregulated diabetes-related genes and pathways in monocytes of diabetes patients with cardiovascular complications, including lncRNA genes of unknown function strongly associated with networks of known diabetes genes.

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.

Plant networks are more connected by invasive brome and native shrub facilitation in Central California drylands

Dominant vegetation in many ecosystems is an integral component of structure and habitat. In many drylands, native shrubs function as foundation species that benefit other plants and animals. However, invasive exotic plant species can comprise a significant proportion of the vegetation. In Central California drylands, the facilitative shrub Ephedra californica and the invasive Bromus rubens are widely dispersed and common. Using comprehensive survey data structured by shrub and open gaps for the region, we compared network structure with and without this native shrub canopy and with and without the invasive brome. The presence of the invasive brome profoundly shifted the network measure of centrality in the microsites structured by a shrub canopy (centrality scores increased from 4.3 under shrubs without brome to 6.3, i.e. a relative increase of 42%). This strongly suggests that plant species such as brome can undermine the positive and stabilizing effects of native foundation plant species provided by shrubs in drylands by changing the frequency that the remaining species connect to one another. The net proportion of positive and negative associations was consistent across all microsites (approximately 50% with a total of 14% non-random co-occurrences on average) suggesting that these plant-plant networks are rewired but not more negative. Maintaining resilience in biodiversity thus needs to capitalize on protecting native shrubs whilst also controlling invasive grass species particularly when associated with shrubs.

Integration of Social Network Analysis in the Evaluation of Advertising Communication Effectiveness and Legal Compliance

A fuzzy genetic algorithm (FGA) is a hybrid optimization technique that combines the principles of fuzzy logic with genetic algorithms (GAs). Both fuzzy logic and genetic algorithms are powerful tools for optimization and decision-making in complex, uncertain environments. A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. This paper proposes an innovative approach to evaluate advertising communication effectiveness and legal compliance by integrating Social Network Analysis (SNA) with Sugeno Fuzzy Genetic Network Analysis (SF-GNA). By combining SNA's insights into communication dynamics and SF-GNA's ability to model complex relationships, the proposed framework offers a comprehensive assessment of advertising campaigns. The integration of SNA enables the identification of influential nodes and communication pathways within social networks, shedding light on the dissemination and reception of advertising messages. Concurrently, SF-GNA facilitates the analysis of legal compliance by assessing the fuzzy relationships between advertising content and regulatory requirements. Social Network Analysis identified key influencers within the network, with nodes A, B, and C demonstrating high centrality scores of 0.85, 0.79, and 0.76, respectively. SF-GNA evaluated the effectiveness of advertising messages, with a membership value of 0.92 indicating strong alignment with consumer preferences and engagement. SF-GNA analyzed the compliance of advertising content with regulatory standards, yielding a compliance score of 0.88, indicating adherence to legal requirements. With Specific regulatory aspects, such as truthfulness and data privacy, were evaluated with membership values of 0.90 and 0.85, respectively, indicating robust compliance.

An Application of Innovative Algorithm of Integrated Social Network Analysis with Statistical Lstm Chain Network Analysis (Slstm-Cna) for Entrepreneurial Team Member Selection

Selecting the right members for an entrepreneurial team is a critical step in ensuring the success and viability of a startup venture. Each team member brings unique skills, experiences, and perspectives that contribute to the overall vision and execution of the business idea. The factors such as cultural fit, adaptability, and a shared commitment to the venture's mission and values can foster cohesion and synergy within the team. This paper presents an innovative algorithm for the selection of entrepreneurial team members, Leveraging insights from social network dynamics and LSTM-based predictive modeling, the algorithm aims to identify individuals with the highest potential for contributing to the success of a startup venture. By analyzing social network structures, the algorithm identifies key influencers and connectors within professional networks, thereby facilitating the identification of candidates who possess valuable connections and collaborative capacities. Furthermore, SLSTM-CNA enables the algorithm to forecast future network trends and dynamics, aiding in the selection of team members who can adapt and thrive in evolving entrepreneurial ecosystems. simulated entrepreneurial ecosystem, the algorithm identified individuals with the highest potential for contributing to startup success based on various criteria, including network centrality, connectivity, and predictive network dynamics. For instance, individuals with a centrality score exceeding 0.7 demonstrated a 25% higher likelihood of facilitating valuable connections critical for business growth. Additionally, candidates identified through SLSTM-CNA exhibited a 30% increase in adaptability to changing network structures compared to those selected through conventional methods