Edge Weights Research Articles

Network Analysis of Internalizing and Externalizing Symptoms in Arab Gifted Children: A Cross-Sectional Study.

The current paper aimed to estimate the network structure of general psychopathology (internalizing and externalizing symptoms/disorders) among 239 gifted children in Jordan. This cross-sectional study with a convenience sampling method was conducted between September 2023 and October 2024 among gifted children aged 7-12. The Child Behavior Checklist (CBCL) was employed to assess six symptom clusters: conduct problems, attention-deficit/hyperactivity disorder (ADHD), and oppositional defiant problems as externalizing symptoms, and affective problems, anxiety issues, and somatic complaints as internalizing symptoms. We used the network analysis perspective by graphical least absolute shrinkage and selection operator (gLASSO) and the Extended Bayesian Information Criterion (EBIC). These methods were used to determine network structure and important nodes in the estimated network. "Sleeps less" (centrality strength = 2.04, edge weight = 0.33) was the central symptom in the affective cluster. In contrast, "worries" (centrality strength = 1.89, edge weight = 0.28) and "headaches" (centrality strength = 2.35, edge weight = 0.41) were pivotal in the anxiety and somatic clusters, respectively. The findings suggested that these symptoms had critical roles in the context of the general psychopathology among gifted children. Accordingly, the mentioned symptoms should be assessed and targeted among gifted children. Future studies could evaluate the results of targeting these symptoms on gifted children's well-being and daily functions.

Adapting to the stream: an instance-attention GNN method for irregular multivariate time series data

Multivariate time series (MTS) data are vital for various applications, particularly in machine learning tasks. However, challenges such as sensor failures can result in irregular and misaligned data with missing values, thereby complicating their analysis. While recent advancements use graph neural networks (GNNs) to manage these Irregular Multivariate Time Series (IMTS) data, they generally require a reliable graph structure, either pre-existing or inferred from adequate data to properly capture node correlations. This poses a challenge in applications where IMTS data are often streamed and waiting for future data to estimate a suitable graph structure becomes impractical. To overcome this, we introduce a dynamic GNN model suited for streaming characteristics of IMTS data, incorporating an instance-attention mechanism that dynamically learns and updates graph edge weights for realtime analysis. We also tailor strategies for high-frequency and low-frequency data to enhance prediction accuracy. Empirical results on real-world datasets demonstrate the superiority of our proposed model in both classification and imputation tasks.

A novel voting measure for identifying influential nodes in complex networks based on local structure

Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy.

Study on the Identification of Terminal Area Traffic Congestion Situation Based on Symmetrical Random Forest

As the demand for air transport continues to increase, air traffic congestion in the terminal area is becoming more and more serious. In order to assist the controller in efficiently handling the symmetrical activities of aircraft take-off or landing and alleviate traffic congestion, this paper proposes a method for identifying traffic congestion situations based on complex networks and a multiclass random forest algorithm with symmetrical characteristics. First, the approach points, departure points, waypoints, and navigation stations are used as nodes, the flight paths as edges, and the busyness of the paths as edge weights to construct a traffic network model for the terminal area. On this basis, five congestion situation recognition indicators are selected from the perspective of network topology, and a symmetric multiclass random forest algorithm is proposed to recognize the congestion situation. Finally, this method is compared with the situation recognition method based on the traditional random forest algorithm. The results of the simulation experiment show that compared with the traditional random forest algorithm, the proposed recognition model improves the recognition accuracy by 17.5%, can better handle symmetry information, and can accurately determine the traffic congestion situation in the terminal area.

DSTSPYN: a dynamic spatial-temporal similarity pyramid network for traffic flow prediction

Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.

A Network Analysis of Quality of Life Among Older Adults With Arthritis.

Arthritis, one of the most common chronic diseases among older people, greatly impairs quality of life through a variety of physical and psychological challenges. This study used network analysis to gain a deeper understanding of the relationships between the indicators of quality of life in older adults with arthritis depending on duration of disease. This cross-sectional study used data from 874 older adults with osteoarthritis and/or rheumatoid arthritis who answered the eighth (2019-2021) Korea National Health and Nutrition Examination Survey. We used network analyses of these data to investigate eight indicators of quality of life (pain, climbing stairs, vitality, working, depression, sleep, happiness and memory loss). Participants were divided into two groups depending on the duration of their arthritis (group A: < 10 years, group B: ≥ 10 years). For group A, depression, working and climbing stairs were the most central indicators affecting quality of life. For group B, the most central quality of life indicators were depression, working, happiness, pain and sleep. In group A, the strongest associations were between depression and happiness, pain and climbing stairs and working and climbing stairs. The same strong associations were observed in group B, along with additional strong associations between depression and memory loss, sleep and happiness, pain and working and happiness and vitality. The bootstrap analyses showed that the networks were very stable and the edge weights accurately estimated. Our findings suggest that healthcare professionals should routinely screen for depressive symptoms and activities of daily living, especially for older people with 10 or more years of arthritis. Both psychological and physical indicators should be prioritised as key factors in self-management interventions that aim to improve quality of life for older adults with arthritis. Sejong-si, South Korea: Implications for Practice. This study highlights the importance of multidimensionalcare plans to improve the quality of life for older adults with arthritis. Healthcareprofessionals should adopt care strategies that simultaneously address the key psychologicaland physical indicators to effectively enhance overall quality of life. This study highlights the importance of multidimensional care plans to improve the quality of life for older adults with arthritis. Healthcare professionals should adopt care strategies that simultaneously address the key psychological and physical indicators to effectively enhance overall quality of life.

WePred: Edge Weight-Guided Contrastive Learning for Bipartite Link Prediction

Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links as binary connections, and they struggle to capture complex interaction patterns in sparse networks. We propose WePred, a weight-guided contrastive learning method for link prediction which addresses both challenges through three key components: (1) a weight-guided edge attention mechanism that incorporates edge weights into neighbor aggregation using dynamic attention scores, enabling the fine-grained capture of interaction strengths; (2) a dual-level contrastive learning approach that combines edge-level and node-level contrasts to capture both local weighted patterns and global structural dependencies, which is particularly effective in sparse regions; and (3) a unified learning framework that integrates classification and contrastive objectives. We evaluate WePred on five real-world datasets. The experimental results show that WePred consistently outperformed state-of-the-art methods, achieving improvements of 7.27% in the AUC on ML-1M and 3.18% in the AUC on the sparsest dataset of Amazon-Book (density: 0.001%). Ablation studies confirm the effectiveness of each component, while parameter analyses provide deployment guidance.

Unveiling relationships of human instinctive behaviors and blood pressure in Chinese adults: A network analysis.

Human behaviors and blood pressure (BP) are closely intertwined, affecting health. However, few studies have demonstrated their complex relationships. Using network analysis, we aimed to untangle the intricate and potential causal relationships between BP and human instinctive behaviors, identifying potential action points for hypertension prevention and control. Cohort study. This study is based on two-wave data from 11,277 Chinese adults in the China Multi-Ethnic Cohort (CMEC) and an independent validation cohort of 21,947 adults from the Chinese Cohort of Working Adults (CCWA). We conducted a cross-sectional network analysis to estimate the interrelationships between measured BP and self-reported physical activity (PA), diet, and sleep. The centrality of each variable within the network was estimated using strength centrality index. Additionally, we performed a longitudinal network analysis to infer the potential causal relationships between BP and human instinctive behaviors in participants with normal or above-normal BP. The network models suggested that BP exhibited negative associations with the dietary score (edge weight: -0.09) and sleep duration (-0.05). Non-sedentary behavior and dietary score were the most central variables, strongly connecting BP and other lifestyles. Higher BP predicted a poorer dietary score (β=-0.21) in participants with above-normal BP. The effect of BP on dietary score was replicated in the validation cohort and validated by linear regression analysis. Non-sedentary behaviors and dietary behaviors may be the potential action points in promoting healthy behaviors and contributing to BP management. It is essential to manage individuals' BP status, non-sedentary behaviors, and dietary behaviors to disrupt malignant cycles.

Когнитивные рефрейминги в предвидении и предотвращении мультиплексных угроз критической инфраструктуре

The article introduces a new cognitive reframing approach to anticipating and preventing multiplex threats to critical infrastructure. In the context of constantly evolving threats, the model may increase the effectiveness of incident prevention strategies. It is visualized as a graph with nodes for concepts of cognitive reframing and edges for the connections between them. The model includes weight values that depend on the importance of each concept, as well as additional importance metrics, coefficients, and interactions. By calculating the edge weights, the authors developed a graph that illustrates the interrelationships between the concepts. The model can be applied to various scenarios as it improves cybersecurity, responds to natural disasters, and ensures the smooth operation of various systems. The model takes into account dynamic factors, multiple importance metrics, interactions, and statistical methods, which makes it flexible and adaptive. Extra factors could increase the complexity, accuracy, and adaptability of the current model. Cognitive reframing has good prospects in the field of critical infrastructure while the new model proves to be an effective threat management tool.

Analysis of type 2 diabetes mellitus-related genes by constructing the pathway-based weighted network.

Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway-based weighted network model of T2DM-related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.

Using Network Analysis of Adolescent Self-Ratings of ADHD Symptoms to Identify Central Symptoms and Their Associations With Each Other and Global Functioning.

This research examined the network properties (network graph, centrality, and edge weights) of the 18 ADHD symptoms, based on the self-ratings of 300 adolescents. The findings indicated the three symptoms with the highest centrality values were "inattention", "wait", and "interrupt". For edge weights, there were positive large effect size associations for "lose" with "forgetful", "fidget with "run", "blurt" with "wait", and "wait" with "interrupt"; and positive moderate effect size associations for "careless" with "instruction", and "avoid" with "listen". Five IA symptoms ("careless", "instruction", "avoid", "distracted", and "forgetful") and one HI symptom ("quiet") were associated negatively and significantly with global functioning. Overall, these associations and relations should be prioritized when planning treatment for ADHD. This is the first study to examine the network properties of ADHD symptoms for adolescent self-ratings. The implications of the findings for theory include a better understanding of the relationships and interrelations between ADHD symptoms, especially in terms of the clustering of IA and HI symptoms and their associations with global functioning. In practice, the findings indicate there are different symptoms that could be the focus for assessment and treatment according to the ADHD presentation type.

A simulated annealing algorithm for randomizing weighted networks.

Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.

SuperSpot: coarse graining spatial transcriptomics data into metaspots.

Spatial Transcriptomics is revolutionizing our ability to phenotypically characterize complex biological tissues and decipher cellular niches. With current technologies such as VisiumHD, thousands of genes can be detected across millions of spots (also called cells or bins depending on the technologies). Building upon the metacell concept, we present a workflow, called SuperSpot, to combine adjacent and transcriptomically similar spots into "metaspots". The process involves representing spots as nodes in a graph with edges connecting spots in spatial proximity and edge weights representing transcriptomic similarity. Hierarchical clustering is used to aggregate spots into metaspots at a user-defined resolution. We demonstrate that metaspots reduce the size and sparsity of spatial transcriptomic data and facilitate the analysis of large datasets generated with the most recent technologies. SuperSpot is an R package available at https://github.com/GfellerLab/SuperSpot and archived on Zenodo (https://doi.org/10.5281/zenodo.14222088). The code to reproduce the figures is available at https://github.com/GfellerLab/SuperSpot/tree/main/figures (https://doi.org/10.5281/zenodo.14222088).

NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction

Predicting ncRNA–protein interactions (NPIs) is essential for understanding regulatory roles in cellular processes and disease mechanisms, yet experimental methods are costly and time-consuming. In this study, we propose NPI-WGNN, a novel weighted graph neural network model designed to enhance NPI prediction by incorporating topological insights from graph structures. Our approach introduces a bipartite version of the high-order common neighbor (HOCN) similarity metric to assign edge weights in an ncRNA–protein network, refining node embeddings via weighted node2vec. We further enrich these embeddings with centrality measures, such as degree and Katz centralities, to capture network hierarchy and connectivity. To optimize prediction accuracy, we employ a hybrid GNN architecture that combines graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE layers, each contributing unique advantages: GraphSAGE offers scalability, GCN provides a global structural perspective, and GAT applies dynamic neighbor weighting. An ablation study confirms the complementary strengths of these layers, showing that their integration improves predictive accuracy and robustness across varied graph complexities. Experimental results on three benchmark datasets demonstrate that NPI-WGNN outperforms state-of-the-art methods, achieving up to 96.1% accuracy, 97.5% sensitivity, and an F1-score of 0.96, positioning it as a robust and accurate framework for ncRNA–protein interaction prediction.

Statistical Analysis of Complex Shape Graphs.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge lies in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.

Network analysis of the hair-based nine hormones from four neuroendocrine systems.

The stress response maintains the homeostasis of the body's internal environment and normal physiological activities, involving several neuroendocrine systems, such as the HPA axis, the HPG axis, the endocannabinoid system, and the melatonin system. However, studies on the intricate interactions among the four neuroendocrine systems are lacking, and it is not clear how these interactions are affected by demographic variables. The aim of this study was to investigate the network characteristics of hormonal networks comprising nine hormones from four neuroendocrine systems and how they were affected by demographic variables. 252 healthy current students were recruited from Southeast University, China. The concentrations of nine hormones in their hair were measured by LC/MS methods, and hormonal network was constructed. Network analysis was used to characterize the interrelationships between hormones or neuroendocrine systems, central hormones, bridge hormones, hormonal network characteristics, and their changes in response to demographic variables. Complex interactions between the HPA axis, the HPG axis, the ECS and the melatonin system formed a sparse and stable network, with cortisol and cortisone being the central hormones and melatonin as the bridge hormone. Demographic variables did not affect the overall characteristics of the network or the central hormone, but a number of specific connections in the network changed and the bridge hormones became cortisone and progesterone. The interactions between the four stress-related neuroendocrine systems were relatively stable and were centered and initiated by the HPA axis. Demographic variables did not affect the overall structure of the network, but influenced local features of the network, such as edge weights and bridge centrality.

Hybrid Algorithm Combining Bellman-Ford, Dijkstra, and Machine Learning for Dynamic Network Routing

Abstract: Optimizing routing in dynamic networks has emerged as an increasingly important problem today with re- duced latency, guaranteed connectivity and efficient data trans- fer under a variety of changing conditions. Among the more well known shortest path algorithms those of Bellman-Ford and Dijkstra’s have very notable advantages and disadvantages themselves; while Bellman-Ford has provisions against negative weighted edges and pays a penalty in computation, Dijkstra’s algorithm cannot work with negative weighted edges but is highly efficient. It introduces a new hybrid routing algorithm that makes intelligent choices between the classical Bellman-Ford and Dijk- stra’s using machine learning techniques. This model, through historical analysis of data on traffic data and network metrics, predicts the level of congestion and identifies that algorithm which is most efficient in the selection of a call for routing. It switches over to Bellman-Ford if congestion expectation is high or if it contains negative weights, whereas under stable network conditions, it employs Dijkstra’s due to its efficiency. Preliminary experiments reveal that this adaptive strategy optimizes both computation overhead and route selection accuracy in large scale dynamic networking environments. Our results indicate improvements in routing efficiency in complex networks based on predictive analytics from established algorithms

The association of quality of life and fear of progression in patients with pulmonary hypertension: a cross-sectional network analysis

BackgroundThe quality of life (QoL) of patients with pulmonary hypertension (PH) is severely compromised. Fear of progression (FoP) may be an important predictor of compromising the QoL in PH patients. Therefore, our study aimed to investigate the association between QoL and FoP among PH patients.MethodsA cross-sectional study comprising 255 patients was conducted using convenience sampling, using the Self-administered socio-demographic information questionnaire, the World Health Organization quality of life questionnaire abbreviated version, and the Chinese version of the Fear of Progression Questionnaire-Short Form. Network analysis was completed using R software.ResultsIn the current network model, the QoL in the psychological domain had the highest node centrality (strength = 1.155). The highest bridge centrality node was physiological health-related fear (bridge strength = 0.303). The edge between physiological health-related fear and social family-related fear has the highest strength (edge weights = 0.596).ConclusionsThis study revealed that the psychological domain of QoL is likely to be the most dominant in the overall QoL of PH patients. It can serve as a primary focus for QoL management strategies to improve the overall QoL of PH patients. Furthermore, physical health-related fear is an urgent mental health concern to be alleviated in PH patients' FoP. As a bridge variable, physical health-related fear can be used as a potential clinical intervention target for developing and utilizing intervention programs to alleviate the FoP in PH patients, which may help maintain or even enhance the psychological and overall QoL of PH patients.

Gaussian level-set percolation on complex networks.

We present a solution of the problem of level-set percolation for multivariate Gaussians defined in terms of weighted graph Laplacians on complex networks. It is achieved using a cavity or message passing approach, which allows one to obtain the essential ingredient required for the solution, viz. a self-consistent determination of locally varying percolation probabilities. The cavity solution can be evaluated both for single large instances of locally treelike graphs, and in the thermodynamic limit of random graphs of finite mean degree in the configuration model class. The critical level h_{c} of the percolation transition is obtained through the condition that the largest eigenvalue of a weighted version B of a nonbacktracking matrix satisfies λ_{max}(B)|_{h_{c}}=1. We present level-dependent distributions of local percolation probabilities for Erdős-Rényi networks and and for networks with degree distributions described by power laws. We find that there is a strong correlation between marginal single-node variances of a massless multivariate Gaussian and local percolation probabilities at a given level h, which is nearly perfect at negative values h, but weakens as h↗0 for the system with power-law degree distribution, and generally also for negative values of h, if the multivariate Gaussian acquires a nonzero mass. The theoretical analysis simplifies in the case of random regular graphs with uniform edge weights of the weighted graph Laplacian of the system and uniform mass parameter of the Gaussian field. An asymptotic analysis reveals that for edge weights K=K(c)≡1 the critical percolation threshold h_{c} decreases to 0, as the degree c of the random regular graph diverges. For K=(c)=1/c, however, the critical percolation threshold h_{c} is shown to diverge as c→∞.

Disentangling the relationships of health-related quality of life, depressive symptoms, disability and social support after stroke: A network analysis

BackgroundHealth-related quality of life (HRQOL), depressive symptoms, disability, and social support show complex interdependences after stroke, which cannot be sufficiently depicted by commonly used uni- or bivariate analyses. Applying a network analysis, we aim to disentangle these multivariate relationships and deduce meaningful starting points for interventions. MethodsStroke survivors (N = 202) were recruited from two inpatient rehabilitation clinics. Participants self-reported mental and physical HRQOL, depressive symptoms, disability, and social support. We computed a partial correlation network and included these five variables as separate nodes. We estimated edge weights, node centrality (expected influence), node predictability, and clusters. Bootstrap methods were applied to assess network stability. ResultsDepressive symptoms and mental HRQOL were the most central and interconnected nodes in the network. Depressive symptoms built its own cluster. Social support showed a high association with depressive symptoms. Disability had no significant associations with other nodes in the network. Physical HRQOL was significantly connected only to its mental equivalent. LimitationsThe cross-sectional design limits the findings to the setting of inpatient rehabilitation few weeks after stroke and allows no longitudinal inferences. The relatively small sample size and varying metrics of applied measures are counterbalanced by a high stability of estimations. ConclusionsDepression and social support show stronger associations with HRQOL than physical aspects during stroke inpatient rehabilitation. This underscores the significance of mental aspects shortly after stroke. Development and implementation of early interventions targeting depressive symptoms and social support may sustainably mitigate the burden on HRQOL after stroke.