Application Of Network Analysis Research Articles

An efficient network clustering approach using graph-boosting and nonnegative matrix factorization

Network clustering is a critical task in data analysis, aimed at uncovering the underlying structure and patterns within complex networks. Traditional clustering methods often struggle with large-scale and noisy data, leading to suboptimal results. Also, the efficiency of positive samples in network clustering depends on the carefully constructed data augmentation, and the pre-training process of the model deals with large-scale data. To address these issues, in this paper, we introduce an efficient network clustering approach that leverages Graph-Boosting and Nonnegative Matrix Factorization to enhance clustering performance (GBNMF). Our algorithm addresses the limitations of traditional clustering techniques by incorporating the strengths of graph-boosting, which iteratively improves the quality of clusters, and Nonnegative Matrix Factorization (NMF), which effectively captures latent structures within the data. We validate our algorithm through extensive experiments on various benchmark network datasets, demonstrating significant improvements in clustering accuracy and robustness. The proposed algorithm not only achieves superior clustering results but also exhibits remarkable computational efficiency, making it a valuable tool for large-scale network analysis applications.

Measuring the adaptive capacity of rangeland users under drought stress in North-easternIran: application of social network analysis

Drought events have significant impact on ecosystems and the livelihoods of rural communities in Iran. So, the purpose of this study was the analysis and evaluation of dimensions of adaptive capacity (AC) against drought in Bajestan county, Khorasan Razavi province, Iran. A questionnaire was used to collect data for social network analysis (SNA) and AC by the full network method. Then, a combined SNA-SEM model was developed to determine what was the social response of rangeland users in dealing with drought. In this study, the AC of rangeland users was compared in two groups of villages covered by the collaborative management or Carbon Sequestration Project (CSP) and uncovered by CSP. The results confirmed that the villages under CSP had higher levels of AC and social capital. Among five capitals, indicators of economic resources and information, skills and management were more effective on AC. Among the SNA indexes, effsize, constrain, indirects, density and betweenes centrality are more effective on AC of rangeland users. Results showed that identifying the key actors, who have the power and influence to determine the information flow and financial inflows in these components, can help increase the AC of rangeland users to mitigate effects of drought.

A multiple behaviour temporal network analysis for health behaviours during COVID-19.

The aim of this study was to examine the temporal dynamics of multiple health behaviours (physical activity, alcohol consumption, healthy eating, cigarette consumption, recreational drug use, vaping), and pandemic-related health behaviours (e.g., hand washing, physical distancing) using network psychometrics. The International COVID-19 Awareness and Responses Evaluation (iCARE) study is an international multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. A sub-sample of longitudinal data from Canadians (n = 254) was analysed across four waves (February-July 2020). We used temporal network models to fit temporal networks, contemporaneous networks, and between-subject networks from items within the iCARE survey. Positive temporal associations were observed between physical activity and healthy eating, and a bidirectional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed positive associations between consumption behaviours (vaping, cigarette use, alcohol use, and recreational drug use), and negative associations between physical activity and drug use, and healthy eating and cigarette use. Health behaviours are interconnected and can be modelled as networks or behavioural systems. The application of temporal network analysis to the study of multiple health behaviours is well suited to address key research questions in the field such as 'how do multiple health behaviours co-vary with one another over time'. Future research using time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis-generating insights.

The references of the nations: Introducing a corpus of United Nations General Assembly resolutions since 1946 and their citation network

This article introduces a novel corpus containing all resolutions adopted by the United Nations General Assembly (UNGA) from 1946 to 2019, the network of citations between them, and an online tool for exploring them. Resolutions adopted by the organization provide a valuable record of the evolution of multilateralism and political ideas on the global stage. Given that resolutions typically cite past ones, the resulting network of references offers a wealth of information on the UNGA’s most relevant themes over time. This article applies network analysis to the corpus of approximately 18,000 resolutions and 77,000 citations, aiming to answer questions such as: What are the most cited resolutions of all? Are there thematic clusters? And can we delineate historic patterns based on themes prioritized? Our findings show that leading resolutions, such as the Universal Declaration of Human Rights, stemmed from the early years of the UNGA and created cores around which thematic clusters emerged. We conclude by listing highly cited resolutions and prominent clusters, while also conducting a descriptive exploration of how some of the largest clusters formed. The dominant clusters addressed human rights, development, decolonization and peace and security, and by monitoring their evolution we offer a new, data-intensive account of the interaction between these topics since 1946.

Spatial distribution characteristics of relationship network of Beijing-Hangzhou Grand Canal water engineering facilities based on Gephi

The Beijing-Hangzhou Grand Canal, China’s oldest and most crucial water transportation project, ensures smooth operation and efficient water transportation through interconnected water engineering facilities. Studying the connections among the water engineering facilities of the Beijing-Hangzhou Grand Canal is theoretically and practically significant for preserving and innovating canal heritage. Therefore, this study utilizes social network analysis to comprehensively examine the spatial connections and network status of water engineering facilities along the Beijing-Hangzhou Grand Canal across different channel structure. Gephi 0.10.1 was utilized in this study to construct a relational network of water engineering facilities along the Beijing-Hangzhou Grand Canal, with each facility considered as a network node. By applying network analysis indices such as degree, closeness centrality, and betweenness centrality, the correlation between water engineering facilities was thoroughly investigated. The study’s findings reveal that: (1) the Beijing-Hangzhou Grand Canal possesses numerous overall network nodes with extensive coverage; however, its overall network density is relatively low, and the inter-node connection is weak. (2) Across the entire network, the spatial distribution of degree and betweenness centrality exhibits a clustered pattern. Their distribution patterns are centered on the Lake region section where Hongze Lake is located and the segment from Liucheng to Zhenjiang in the Lake region, the River transport confluence section, and the Nature river section, respectively. The spatial distribution characteristics of closeness centrality show a dispersed shape, with stronger areas mainly concentrated in the canal’s tributaries, especially the Nature river section, which shows more prominence. (3) Analyzed from a channel structure perspective, water engineering facilities in different sections assume distinct linking roles within the network. Facilities in the Lake region section play the strongest overall linkage role, partly due to its highest node proportion. Conversely, in the Nature river section, facilities primarily serve transshipment and direct connection functions, whereas in the River transport confluence section, they act mainly as intermediaries or “bridges”. Notably, water control facilities in the Nature river section and river engineering facilities in the River transport confluence section play pivotal driving roles in their respective sections, warranting special attention and protection as critical canal nodes.

Application of Artificial Neural Network Analysis in Predicting the Performance of Microbial Energy Cells

Objective: The objective of this study was to apply Artificial Neural Networks to evaluate the performance of Microbial Energy Cells, to identify the best network configuration for cell evaluation. Theoretical Framework: Although several of the widely used effluent treatment methods show results, most of them have a common disadvantage: they lose the chemical energy contained in the treated effluent and have high energy consumption for their conduction. Therefore, an increasing effort has been made to develop effluent treatment technologies capable of recovering part of the energy contained in the waste to be treated. In this scenario, microbial energy cells (CEM) emerge as a potential technology, as they are devices that simultaneously treat effluent biologically and generate electrical energy. Methodology: For the application and evaluation of ANNs in CEM, a feedforward neural network was used, with a Levenberg-Marquardt training algorithm, 1 or 2 hidden layers, with sigmoid and tansig activation functions, and an accuracy factor of 10-5. The data used for training and validation for the ANN were obtained through a literature search. Networks with 15, 30, 50, 90, 100, 130, 150, and 200 neurons were used for testing to evaluate the best performance. Results and Discussion: With the results obtained, it was observed that the best adjustment of the network occurred with the 2-layer configuration, one layer with 100 neurons and the other output layer, with 49 interactions and R2 of 0.91 in the training adjustment, 0 .78 in the validation fit and 0.90 in the fit with all experimental data evaluated, respectively. Originality/Value: This study contributes to the literature by evaluating the application of artificial neural networks, which are empirical modeling mechanisms, inspired by biological nervous systems, with processing abilities, in microbial energy cells.

Cortex Mori Radicis[Morus Alba L. (Moraceae)] Extract Alleviates Senescence via PI3K/Akt Signaling in COPD Fibroblasts

BackgroundChronic obstructive pulmonary disease (COPD) is marked by prolonged exposure to cigarette smoke, which accelerates senescence in lung fibroblasts and contributes to lung fibrosis. Cortex Mori Radicis [Morus alba L. (Moraceae)], a traditional Chinese medicinal herb known for its antitussive properties, has emerged as a potential therapeutic agent for COPD. This study aims to elucidate the immunological mechanisms by which Cortex Mori Radicis mitigates COPD progression, utilizing a mouse model and the MRC-5 cell line. Methods and ResultsCOPD mouse models were established through chronic cigarette smoke (CS) exposure, followed by isolation of lung fibroblasts. Senescence markers and inflammatory mediators were assessed in both the isolated cells and the mice. Lung fibroblasts and bleomycin-treated MRC-5 cells exhibited elevated expression of senescence markers, including senescence-associated beta-galactosidase activity, p16INK4A, p21, p38 MAPK, and p53, along with increased levels of senescence-associated secretory phenotype (SASP) mRNA, such as IL-6 and IL-8. Treatment with Cortex Mori Radicis significantly attenuated the protein levels of these senescence markers and reduced SASP mRNA expression. Furthermore, integration of transcriptomic data from lung tissues and primary fibroblasts, combined with network pharmacology analysis, indicated that Cortex Mori Radicis inhibits fibroblast senescence via the PI3K/Akt pathway, thereby ameliorating lung pathology in COPD mice. ConclusionThrough the application of transcriptomics and network analysis, this study identifies that Cortex Mori Radicis suppresses cigarette smoke-induced senescence in pulmonary tissues and bleomycin-exposed MRC-5 cells by targeting the PI3K/Akt signaling pathway. These findings underscore the therapeutic potential of Cortex Mori Radicis as a novel intervention for COPD.

Natural language processing applications in social network analysis: a data mining approach

Natural language processing applications in social network analysis: a data mining approach

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.

Correlation networks in economics and finance: A review of methodologies and bibliometric analysis

AbstractThis paper presents an in‐depth review of the methodologies and innovations in the study of correlation networks in economics and finance. We explore the development of filtering algorithms and distance measures, emphasizing their critical role in extracting meaningful financial interconnections. Our study underscores the relevance of the minimum spanning tree, planar maximally filtered graph, and other advanced tools in interpreting financial dynamics. Empirical insights emphasize the increasing interconnectedness of global financial markets, underscoring the necessity of grasping correlation levels, market structures, and time‐varying dynamics. A notable observation is the marked increase in studies focusing on econometrics, economics, and finance post‐2015, indicating a paradigm shift in research emphasis. Through bibliometric analysis of 1200 publications, we highlight key authorship clusters, the instrumental contributions of individual researchers, trending keywords, and the growing influence of countries like Italy and China. We conclude with an overview of the software tools essential for both academic research and practical applications in financial network analysis.

Leaf Trait Variations and Ecological Adaptation Mechanisms of Populus euphratica at Different Developmental Stages and Canopy Heights

The ability of plants to alter specific combinations of leaf traits during development and in response to abiotic stress is crucial for their success and survival. While there are numerous studies on the variation of leaf traits within the canopies of Populus species, the application of network analysis to understand the variation and combinations of these traits across different growth stages is rare. The leaves of Populus euphratica, a dominant species in arid regions, exhibit notable morphological variations at different developmental stages and canopy heights in response to water scarcity and climate change. In this study, 34 leaf traits (morphological, chemical, photosynthetic, and hydraulic) and their roles in drought adaptation were investigated in 60 Populus euphratica plants at five developmental stages and five canopy heights using leaf trait network (LTN) analysis. The aim was to analyze adaptive strategies to arid environments at different developmental stages and canopy heights through the interdependence of leaf traits. The results showed that the internal coordination capacity of leaf trait networks decreased and then increased with each developmental stage, while the functional modules of leaf trait networks were loosely connected and aggregated with the increase in tree diameter at breast height. With increasing canopy height, the coordination linkage’s ability between leaf traits showed an increasing then decreasing trend, and the traits of the leaves in the canopy at 6 m were more closely connected, less modular, and simpler in topology compared with those in the other layers. Leaves form functional modules by coordinating specific traits that promote growth and resist drought. Leaf photosynthesis, water transport, and nutrient traits were central to different developmental stages, whereas leaf morphology, nutrient metabolism, and drought-resistance-related traits were central to the canopy height. Leaf morphology and osmoregulatory traits play key roles in leaf trait network regulation, including leaf length and width, leaf shape index, soluble sugars, and soluble proteins, which are important “intermediary traits” in the Populus euphratica leaf network. Further analysis revealed that structural traits were important at different developmental stages and canopy heights. When resources are limited, the leaf preferentially maintains a stable connection between structural traits to enhance photosynthesis, and these traits and their combinations might confer drought resistance. During the rapid development stage, the connection between chemical traits becomes important, and the leaf grows by rapidly accumulating nutrients. In summary, this study provides new perspectives and insights into the drought adaptation strategies of P. euphratica at different developmental stages and canopy heights by analyzing leaf trait networks.

Qualitative social network analysis in practice: comparison of methods for network maps construction

This article is devoted to the application of qualitative network analysis on the example of studying the social interactions of young parents in migration. A conceptual description of qualitative network analysis, network visualization features, design of qualitative network research, comparison of two approaches to building network maps and an example of their analysis are presented. Qualitative network analysis is aimed at studying relationships in the network and consists of interpretative and structural components, which are implemented through interviews and building a network map. The two approaches to building network maps differ in that in one case, network maps are built by the researcher based on an interview, and in the other case, the construction of the map is delegated to the informant himself. The first method is limited by the interview data and is their structural visualization. The second method of building a network map by the informant himself creates additional resources for interpretation – contrasting narratives and visual structuring of the informant's interactions.

A SMARTR workflow for multi-ensemble atlas mapping and brain-wide network analysis.

In the last decade, activity-dependent strategies for labelling multiple immediate early gene (IEG) ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally-sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is simple multi-ensemble atlas registration and statistical testing in R (SMARTR), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization. We demonstrate the versatility of SMARTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training (CT), results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region which shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

Gender dynamics and academic rank in emergency medicine collaboration networks: A social network and gender propensity analysis

BackgroundAcademic productivity is bolstered by collaboration, which is in turn related to connectivity between individuals. Gender disparities have been identified in academics in terms of both academic promotion and output. Using gender propensity and network analysis, we aimed to describe patterns of collaboration on publications in emergency medicine (EM), focusing on two Midwest academic departments. MethodsWe identified faculty at two EM departments, their academic rank, and their publications from 2020 to 2022 and gathered information on their co-authors. Using network analysis, gender propensity and standard statistical analyses we assessed the collaboration network for differences between men and women. ResultsSocial network analysis of collaboration in academic emergency medicine showed no difference in the ways that men and women publish together. However, individuals with higher academic rank, regardless of gender, had more importance to the network. Men had a propensity to collaborate with men, and women with women. The rates of gender propensity for men and women fell between the gender ratios of emergency medicine (65%/35%) and the general population (50%/50%), 59.6% and 44%, respectively, suggesting a tendency toward homophily among men. ConclusionOur study aims to use network analysis and gender propensity to identify patterns of collaboration. We found that further work in the area of network analysis application to academic productivity may be of value, with a particular focus on the role of academic rank. Our methodology may aid department leaders by using the information from local analyses to identify opportunities to support faculty members to broaden and diversify their networks.

Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach

Abstract Background Despite impressive dissemination programs of best-practice therapies, clinical psychology faces obstacles in developing more efficacious treatments for mental disorders. In contrast to other medical disciplines, psychotherapy has made only slow progress in improving treatment outcomes. Improvements in the classification of mental disorders could enhance the tailoring of treatments to improve effectiveness. We introduce a multimodal dynamical network approach, to address some of the challenges faced by clinical research. These challenges include the absence of a comprehensive meta-theory, comorbidity, substantial diagnostic heterogeneity, violations of ergodicity assumptions, and a limited understanding of causal processes. Methods Through the application of multimodal dynamical network analysis, we describe how to advance clinical research by addressing central problems in the field. By utilizing dynamic network analysis techniques (e.g., Group Iterative Multiple Model Estimation, multivariate Granger causality), multimodal measurements (i.e., psychological, psychopathological, and neurobiological data), intensive longitudinal data collection (e.g., Ecological Momentary Assessment), and causal inference methods (e.g., GIMME), our approach could improve the comprehension and treatment of mental disorders. Under the umbrella of the systems approach and utilizing e.g., graph theory and control theory, we aim to integrate data from longitudinal, multimodal measurements. Results The multimodal dynamical network approach enables a comprehensive understanding of mental disorders as dynamic networks of interconnected symptoms. It dismantles artificial diagnostic boundaries, facilitating a transdiagnostic view of psychopathology. The integration of longitudinal data and causal inference techniques enhances our ability to identify influential nodes, prioritize interventions, and predict the impact of therapeutic strategies. Conclusion The proposed approach could improve psychological treatment by providing individualized models of psychopathology and by suggesting individual treatment angles.

Clinical utility of the at-risk for psychosis state beyond transition: A multidimensional network analysis.

To be relevant to healthcare systems, the clinical high risk for psychosis (CHR-P) concept should denote a specific (i.e., unique) clinical population and provide useful information to guide the choice of intervention. The current study applied network analyses to examine the clinical specificities of CHR-P youths compared to general help-seekers and non-CHR-P youth. 146 CHR-P (mean age = 14.32 years) and 103 non-CHR-P (mean age = 12.58 years) help-seeking youth were recruited from a neuropsychiatric unit and assessed using the Structured Interview for Psychosis-Risk Syndromes, Children's Depression Inventory, Multidimensional Anxiety Scale for Children, Global Functioning: Social, Global Functioning: Role, and Wechsler Intelligence Scale for Children/Wechsler Adult Intelligence Scale. The first network structure comprised the entire help-seeking sample (i.e., help-seekers network), the second only CHR-P patients (i.e., CHR-P network), and the third only non-CHR-P patients (i.e., non-CHR-P network). In the help-seekers network, each variable presented at least one edge. In the CHR-P network, two isolated "archipelagos of symptoms" were identified: (a) a subgraph including functioning, anxiety, depressive, negative, disorganization, and general symptoms; and (b) a subgraph including positive symptoms and the intelligence quotient. In the non-CHR-P network, positive symptoms were negatively connected to functioning, disorganization, and negative symptoms. Positive symptoms were less connected in the CHR-P network, indicating a need for specific interventions alongside those treating comorbid disorders. The findings suggest specific clinical characteristics of CHR-P youth to guide the development of tailored interventions, thereby supporting the clinical utility of the CHR-P concept.

Finding small feedback arc sets on large graphs

The minimum feedback arc set problem (FASP), which seeks to remove a minimum set of arcs from a directed graph to make the remaining graph acyclic, is fundamental in graph algorithms with many applications in social network analysis, circuit testing, deadlock resolution, algorithmic games, and other fields. However, in theory, FASP is NP-Hard and cannot be approximated within any constant under the Unique-Games Conjecture. In practice, the performance of existing algorithms on large graphs is still unsatisfactory. To solve FASP on large graphs, we propose two novel methods in this paper. First, we systematically study the reduction rules for FASP that can quickly reduce the scale of input graphs without losing optimality. We integrate the reduction rules into an algorithm that can be used as a preprocessor for all existing FASP algorithms to simplify the input graphs. Second, we provide a scalable heuristic algorithm based on the divide-and-conquer method for better solution quality. Extensive experiments on a wide range of real-world graphs show that the reduction algorithm effectively reduces the size of input instances. Furthermore, for half of the circuit benchmark graphs, our final algorithm improves the quality of the existing heuristic solution by 24% to 40% within an acceptable running time.

Positive sexuality, relationship satisfaction, and health: a network analysis.

Positive sexuality, defined as the happiness and fulfillment individuals derive from their sexual experiences, expressions, and behaviors, has been linked to relationship satisfaction and health. However, the intricate associations between positive sexuality and relationship functioning and health indicators have rarely been explored from a network perspective. This approach, by analyzing the interconnections among these factors within a broader system, can offer insights into complex dynamics and identify key variables for targeted interventions. The present study applied network analysis to uncover interconnections between positive sexuality, relationship satisfaction, and health indicators, highlight the most relevant variables and explore potential gender-based differences in a sample of 992 partnered individuals (51% women, aged 18-71 years). Networks were estimated via Gaussian Graphical Models, and network comparison test was used to compare men and women. Results indicated that variables related to positive sexuality were more highly interconnected than the rest of the network. There were small-to-negligible connections between positive sexuality and relationship satisfaction variables, both of which had negligible or no connections with health. The network was globally invariant across gender, though a few connections were gender-specific. The most important variables, regardless of gender, related to pleasurable feelings during sexual intercourse. The findings underscore the importance of enhancing positive sexual experiences within intimate relationships and have implications for research and clinical practice in positive sexuality.

RELATIONAL SEDIMENTS IN THE BACKWASH OF PROTEST WAVES: EXPLORING NETWORK CONSEQUENCES OF COLLECTIVE ACTION

This article addresses the puzzling relational void in the analysis of collective action consequences by exploring how protest waves affect local network structures in two German towns. We compare three periods of time: a preprotest wave latency period, a protest wave period, and a postwave latency period, as defined in relation to contentious collective action around the refugee reception crisis in 2015. Adopting a field perspective, our analysis uses digital communication data to capture the referencing practices between all actors involved in local migration-related protests irrespective of actor type or political orientation. This results in networks of both supportive and antagonistic interaction. Applying network analysis, especially multiple regression quadratic assignment procedures (MRQAP), we find significant and distinct patterns of change and stability for supportive and antagonistic ties. We argue that these findings provide stepping stones for the emergent literature on relational consequences of collective action.

Hazard Identification and Risk Assessment of Biogas Plant with Proton Exchange Membrane Fuel Cell Technology

Biogas-fed proton exchange membrane fuel cell (PEMFC) plants offer a sustainable energy solution, but their operation can pose significant hazards and risks. Ensuring the safety of these plants is paramount, especially given the potential for fires, explosions, and chemical exposures. This study evaluated hazards and risks in biogasfed proton exchange membrane fuel cell (PEMFC) plants using six analytical methods: Dow's fire and explosion index (FEI), Dow's chemical expo-sure index (CEI), Hazard and Operability Study (HAZOP), Risk Matrix Analysis (RMA), Bayesian Network (BN) and ALOHA® software hazard modelling. The FEI analysis revealed that the anaerobic digester and bio-gas storage tank exhibited severe hazards (FEI =170), thereby signifying the highest risks within the plant. CEI analysis revealed the spread of the highest hydrogen sulfide (H2S) concentration up to 129 meters from the anaerobic digester and storage tank location. Further assessment was conducted, calculating risk values using the RMA and performing additional HAZOP analysis specifically for these units. The results confirmed similar risk levels (4-20) between the units, except for a higher explosion risk in the storage tank. The novelty of this research lies in the application of Bayesian Network (BN) analysis. In addition to assessing the hazards associated with PEMFC, our BN analysis reveals that the risk of fire attributed to PEMFC ranges between 10% and 18%, while the risk of explosion falls within the range of 3% to 17%. Based on the hierarchy control concept, several effective mitigation controls were proposed to enhance the safety of biogas-fed PEMFC plants. In future research, a deeper exploration of human error and equipment malfunctions within hazard modelling is crucial for a more precise hazard assessment.