Important Nodes Research Articles

Unsupervised multi-view graph representation learning with dual weight-net

Unsupervised multi-view graph representation learning (UMGRL) aims to capture the complex relationships in the multi-view graph without human annotations, so it has been widely applied in real-world applications. However, existing UMGRL methods still face the issues as follows: (i) Previous UMGRL methods tend to overlook the importance of nodes with different influences and the importance of graphs with different relationships, so that they may lose discriminative information in nodes with large influences and graphs with important relationships. (ii) Previous UMGRL methods generally ignore the heterophilic edges in the multi-view graph to possibly introduce noise from different classes into node representations. To address these issues, we propose a novel bi-level optimization UMGRL framework with dual weight-net. Specifically, the lower-level optimizes the parameters of encoders to obtain node representations of different graphs, while the upper-level optimizes the parameters of the dual weight-net to adaptively and dynamically capture the importance of node level, graph level, and edge level, thus obtaining discriminative fused representations for downstream tasks. Moreover, theoretical analysis demonstrates that the proposed method shows a better generalization ability on downstream tasks, compared to previous UMGRL methods. Extensive experimental results verify the effectiveness of the proposed method on public datasets, in terms of different downstream tasks, compared to numerous comparison methods.

Bridge Nodes Linking Depression and Medication Taking Self-Efficacy Dimensions Among Persons With HIV: A Secondary Data Analysis.

Depression and low medication taking self-efficacy are among the most important mechanisms contributing to poor adherence to treatment and care for persons with HIV (PWH). While the overall negative relationship between depression and medication taking self-efficacy has been well established, little is known on the precise pathways linking depression and medication taking self-efficacy. Thus, it is critical to identify a specific item of depression and medication taking self-efficacy that derives the overall negative relationship. The current study is a secondary data analysis using the baseline data from a randomized controlled trial that aims to support PWH to self-manage antiretroviral therapy regimens via mHealth technology and community health workers to monitor their adherence using a self-management app. A total of 282 participants were included. The machine-learning based network analysis was conducted to explore the structure of the depression and medication taking self-efficacy network and to identify bridge nodes between depression and medication taking self-efficacy. Our study identified difficulty concentrating on things and confidence to stick to treatment schedule when not feeling well are important bridge nodes connecting the network of depression and medication taking self-efficacy. Future studies should focus on developing interventions that would target the bridge pathway and examine their effectiveness in reducing depression and increasing medication taking self-efficacy.

Cross-species brain-wide mapping reveals a conserved and coordinated network engaged by NAc DBS.

Nucleus accumbens (NAc) deep brain stimulation (DBS) has been increasingly explored as a treatment modality for refractory neuropsychiatric disorders. Uncovering the accumbens network that is engaged by DBS is a critical step forward in understanding how modulating this important node impacts the broader mesocorticolimbic circuit. Using whole-brain clearing and unbiased, brain-wide neural activity mapping, we found that NAc DBS increases neural activity in a coordinated mesocorticolimbic network in mice. Simultaneous intracranial electrophysiology recordings from the human NAc and brief stimulation epochs of homologous mesocorticolimbic nodes revealed similar connectivity. Altogether, these results identify specific connectivity conserved across species within the mesocorticolimbic circuit that may underlie mechanisms of NAc DBS.

Cadmium distribution in rice: Understanding the role of plant nodes and growth stages

Cadmium (Cd) contamination in farmland poses a significant threat to food security in staple crops, especially rice. Using a mix of hydroponic and soil culture methods, stable isotope tracers, and advanced analytical techniques, this study elucidated the mechanisms of Cd uptake, translocation, and accumulation in rice throughout different growth stages. Despite a notable linear correlation between soil DTPA (diethylene-triaminepentaacetic acid)-Cd and the total Cd concentration of rice, our findings showed that the influence of soil Cd level on the proportion of Cd in grain was negligible. The study highlighted the dynamic response of Cd distribution within plant nodes to changes in DTPA-extractable Cd. Heading stage (HS) and mature stage (MS) were critical for Cd uptake and upward transport in rice, and the contribution of Cd absorption in brown rice was 28.61% and 40.16%, respectively. Moreover, the distribution of Cd in nodes showed how important nodes are for controlling and redistributing Cd in rice. In the HS, the lower node had a function in re-transporting, whereas in the MS, there was a considerable redistribution of Cd in the upper node.These insights can help us understand rice Cd dynamics and develop agronomic techniques and rice cultivars that minimize Cd accumulation.

Clarifying the relationship between insecure attachment and problematic social media use across platforms: a network analysis.

Despite evidence suggesting that insecure attachment is a significant risk factor for Problematic Social Media Use (PSMU), there remains a lack of comprehensive studies exploring this relationship, and a unified understanding of its role has yet to be established. We employed network analysis to construct an integrated model for examining the complex interrelations between negative emotions, trait and state attachment, motives, and PSMU across three platforms (i.e., WeChat, Sina Weibo, and TikTok), as well as for identifying potential mediating variables between attachment and PSMU. Data were collected from 685 young adults via online self-reported questionnaires. We found that negative emotions are positively correlated with insecure trait and state attachment but have a negligible direct relationship with PSMU. The conformity motive and state attachment security emerged as important central nodes when measured by strength, closeness, and betweenness. Moreover, attachment states and motives were found to be clustered. Such strong interrelationships were also evident between insecure attachment and PSMU, while trait attachment anxiety and avoidance were observed to be related to PSMU across various platforms. Our findings promote a deeper understanding of the relationship between insecure attachment and PSMU from a cross-platform perspective and offer novel insights into the mechanisms underlying their co-occurrence, which may guide the development of effective interventions for healthier social media engagement.

Purity Skeleton Dynamic Hypergraph Neural Network

Recently, in the field of Hypergraph Neural Networks (HGNNs), the effectiveness of dynamic hypergraph construction has been validated, which aims to reduce structural noise within the hypergraph through embeddings. However, the existing dynamic construction methods fail to notice the reduction of information contained in the hypergraphs during dynamic updates. This limitation undermines the quality of hypergraphs. Moreover, dynamic hypergraphs are constructed from graphs. Several key nodes play a crucial role in graph, but they are overlooked in hypergraphs. In this paper, we propose a Purity Skeleton Dynamic Hypergraph Neural Network (PS-DHGNN) to address the above issues. Firstly, we leverage purity skeleton method to dynamically construct hypergraphs via the fusion embeddings of features and topology simultaneously. This method effectively reduces structural noise and prevents the loss of information. Secondly, we employ an incremental training strategy, which implements a batch training strategy based on the importance of nodes. The key nodes, as the skeleton of hypergraph, are still highly valued. In addition, we utilize a novel loss function for learning structure information between hypergraph and graph. We conduct extensive experiments on node classification and clustering tasks, which demonstrate that our PS-DHGNN outperforms state-of-the-art methods. Note on real-world traffic flow datasets, PS-DHGNN demonstrates excellent performance, which is highly meaningful in practice.

Communities of Collaboration: Building the Maria Edgeworth Letters Project

Abstract: The Maria Edgeworth Letters Project ( MELP ) is a digital, searchable, open-access archive of Edgeworth's correspondence that is building a collaborative international community, simulating the sprawling network encompassed in her letters, and demonstrating how Edgeworthstown in Ireland became an important node in the exchange and production of knowledge in the Romantic period. With more than 15,000 manuscript pages from over thirty archives, MELP crowdsources transcriptions through the Zooniverse platform, edits and encodes them through collaborative workflows across five universities, and assembles taxonomies of people, places, and works mentioned in the letters in order to create a sustainable digital project.

A reconstruction method for harmonic measurement in distribution networks based on equivalent harmonic source approach

Abstract In recent years, with a large number of nonlinear devices connected to the distribution network, harmonic problems have been inevitably generated during operation, which makes harmonic problems more complex in the distribution network. However, it is important to master the real-time harmonic level of the distribution network for the analysis and management of the harmonic problems in the grid. However, because of economic reasons and decentralized nodes, it is impractical to install a harmonic measurement device at each node in engineering. Moreover, it is important to know how to measure harmonics at important nodes in a distribution network using a limited number of measurement devices. Therefore, a harmonic measurement reconstruction method for distribution networks is proposed based on the equivalent harmonic source method. This method aims to obtain more harmonic source state information with a minimum economic cost. First, a mathematical model of the harmonic currents is established. Subsequently, an equivalent harmonic source method is proposed, and a harmonic reconstruction model based on the equivalent harmonic source method is presented. Finally, an example of an IEEE 14-node distribution network is used to verify the effectiveness of the equivalent harmonic source method.

Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of Graph Convolutional Networks (GCNs) with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of GCNs that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model’s ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.

Domestic Activities and Culinary Practices at Lomas Entierros, Costa Rica: A Perspective from Chemical Residues, Starch Grain Analyses, and Micro-archaeology

AbstractThe Lomas Entierros archaeological site provides a case study of domestic activities in two socioeconomic sectors. Located in Central Pacific Costa Rica, it was a primary center and important node for the exchange of goods throughout the region. In this article, we characterize and compare the domestic and socioeconomic activities at two structures from different sectors of the site through the analysis of micro-remains, chemical residues (phosphates, carbonates, pH, carbohydrates, protein residues, and fatty acids), and starch grains. Our findings show that differences between the two structures were determined both by their function and the socioeconomic status of their occupants. Structure 13, in the elite sector, presents a richer dataset that suggests the cooking of plants and mollusks in the interior of the dwelling. Structure 44, in the intermediate-status sector, has a lower diversity and density of remains, suggesting very low use that may result from its role as a storage space. This article provides a nuanced methodology for the study of domestic spaces in tropical areas.

Data Source Importance Evaluation for Highway Networks: A Complex Network-Based Approach

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Identification of Drought Stress-Responsive Genes in Rice by Random Walk with Multi-Restart Probability on MultiPlex Biological Networks.

Exploring drought stress-responsive genes in rice is essential for breeding drought-resistant varieties. Rice drought resistance is controlled by multiple genes, and mining drought stress-responsive genes solely based on single omics data lacks stability and accuracy. Multi-omics correlation analysis and biological molecular network analysis provide robust solutions. This study proposed a random walk with a multi-restart probability (RWMRP) algorithm, based on the Restarted Random Walk (RWR) algorithm, to operate on rice MultiPlex biological networks. It explores the interactions between biological molecules across various levels and ranks potential genes. RWMRP uses eigenvector centrality to evaluate node importance in the network and adjusts the restart probabilities accordingly, diverging from the uniform restart probability employed in RWR. In the random walk process, it can be better to consider the global relationships in the network. Firstly, we constructed a MultiPlex biological network by integrating the rice protein-protein interaction, gene pathway, and gene co-expression network. Then, we employed RWMRP to predict the potential genes associated with rice tolerance to drought stress. Enrichment and correlation analyses resulted in the identification of 12 drought-related genes. We further conducted quantitative real-time polymerase chain reaction (qRT-PCR) analysis on these 12 genes, ultimately identifying 10 genes responsive to drought stress.

Analysis of factors influencing medical students’ learning engagement and its implications for teaching work—— a network analysis perspective

PurposeHigher medical education has always been a major project in the fields of education and health, and therefore, the quality of education has received much attention. Learning engagement has emerged as a significant indicator of teaching quality, attracting considerable research attention. This study aims to explore the relationship between medical students’ learning engagement and their sense of school belonging, professional identity, and academic self-efficacy.MethodsWe conducted an online survey using convenience sampling method with 311 medical students. We employed Revised version of the Utrech Work Engagement Scale-Student (UWES-S), Chinese version of the Psychological Sense of School Membership (PSSM) scale, Academic Self-Efficacy Scale, and the questionnaire of college students’ speciality identity for evaluation. Network analysis was used to analyze the relationships among these factors.ResultsMedical students’ overall performance in school showed a positive trend. However, there is still room for improvement. In the network structure of learning engagement and its influencing factors, the “emotional” aspect of professional identity (EI = 1.11) was considered to be an important node with strong centrality. And “academic competence self-efficacy” aspect of academic self-efficacy (BEI = 0.72) was considered an important node with strong transitivity.ConclusionDeepening medical students’ emotional identification with their profession and enhancing their confidence in their academic abilities may improve their learning engagement and educational quality.

The Sex Worker That She Is Not: Suggestion, Risk and Public Femininity in Contemporary Hindi Cinema

This paper argues for the significance of sex work as a figurative and affective reference point in the production of ideas of romance, risk and respectability in public culture through a reading of contemporary Hindi cinema. Reading the films Jab We Met (2007) and Pink (2016), I ask what the sex worker does in the films’ narratives when she herself is absented from it but brought up suggestively. I propose suggestion as a figurative technique deployed in the films to mediate a tension about knowing and identifying sex work as well as to produce an ideology of romance, personhood and injury, critical to assemblages of femininity in these films. This paper argues that such a reading of films that privileges the figuration of sex work through what may otherwise be seen as a minor invocation, presents the opportunity to critically examine naturalised ideas of romance, risk and respectability that find expression in them. It makes the case for the figure of the sex worker as an important node of affective intensification in the larger sphere of public sexuality.

Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study

Network centrality analyses have proven to be successful in identifying important nodes in diverse host–pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein–protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of the SARS-CoV-2 targets in the human interactome, with closeness centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved to be effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host’s cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.

Node ranking algorithm using Graph Convolutional Networks and mini-batch training

This paper presents a novel algorithm for ranking nodes in graph-structured data using Graph Convolutional Networks (GCNs) combined with mini-batch training. The proposed method integrates local and global structural information, enabling a comprehensive understanding of node importance within complex networks. By employing a multi-layer GCN architecture with residual connections and dropout regularization, our approach captures intricate graph patterns while mitigating common issues such as vanishing gradients and overfitting. The node importance scores are computed using a Multi-Layer Perceptron (MLP), with the entire model trained using Mean Squared Error (MSE) loss optimized via the Adam algorithm. We demonstrate the scalability and effectiveness of our method through extensive experiments on various benchmark datasets, showcasing its superior performance in node ranking tasks compared to existing approaches.

Reflections on the production of space and justice: A study of everyday life of the SuoJincun's guerrilla gardeners

As a country that legally guarantees equal access to public space for all residents, China still faces the challenge of abstract public power impeding socio-spatial production and the realization of spatial justice. The article focuses on two case studies of community guerrilla gardening in Nanjing. It employs ethnographic research methods to analyze the green grassroots actions carried out by residents in the community and to demonstrate the differentiated representational spatial production of the residents. The results show that: gardening has an irreplaceable social acquisition value as the production of real matter, is a material carrier and bond of place attachment, and a means of shaping cultural identity. It contains the potential for creative gardening methods and is an important node of access to nature and culture for groups with limited mobility. Guerrilla gardening recycling of collective space by residents in the form of private appropriation. The discussion session analyzes the positive side of the phenomenon of resident's privatization of common public space from the perspectives of social change, the value of use of public space, and spatial justice. The concludes by moving away from the discussion of public and private space toward an everyday, use-value-driven, locally creative initiative for a new aesthetics of collective space.

The role of hyperarousal for understanding the associations between sleep problems and emotional symptoms in family caregivers of people with dementia. A network analysis approach.

Caregiving for a family member with dementia is a stressful situation that has been associated with symptoms of depression, anxiety, and insomnia. Several models have highlighted the role of hyperarousal for understanding sleep disorders; however, there is little evidence about how insomnia, depression, and anxiety are linked together. Network analysis could help to explore the mechanisms underlying the associations between these disorders. A total of 368 community-dwelling family caregivers of a person with dementia took part in the study. The depression-anxiety-sleep symptoms network was composed of 26 items using the R package qgraph to estimate and visualise the network. The results showed that the strongest symptoms in the network were shakiness, tension, restlessness, nervousness, and restless sleep. Tension was the symptom with the most predictive power, restless sleep was the most important shortcut node in the connection between other symptoms. The central stability coefficient showed adequate indices. The strength of hyperarousal symptoms suggested a prominent role of this variable. Our results invite the hypothesis that sleep problems may trigger symptoms specific to depression via fatigue or energy loss. This study is the first to examine the network structure of the associations between the symptoms of depression, anxiety, and insomnia in a sample of informal caregivers, and to explore the role of hyperarousal in this network.

Node Centrality Based on Edge Dynamics in a Chaotic Network

This paper introduces a novel node centrality index based on edge dynamics in a complex network: adjacent edge index. Through the analysis of network dynamics and topology, we provide a neat index to qualify node importance, which is calculated by the eigenvector components corresponding to the Fiedler value of the network. Notably, it identifies nodes whose adjacent edge sets play a significant role in network synchronization. The index indicates that not only a node’s degree but also its location should be considered. To demonstrate the effectiveness of the proposed index, consider a double-star network and a Jazz Musicians network in which chaotic Chua’s circuits as nodes are coupled. By factoring in the dynamic information exchange between nodes and their neighbors, this index offers valuable insights for optimizing strategies in engineering areas such as efficient information dissemination.