Important Nodes Research Articles

Dispersion network-transition entropy: A metric for characterizing the complexity of nonlinear signals.

Extracting meaningful information from signals has always been a challenge. Due to the influence of environmental noise, collected signals often exhibit nonlinear characteristics, rendering traditional metrics inadequate in capturing the dynamic properties and complex structures of signals. To address this challenge, this study proposes an innovative metric for quantifying signal complexity-dispersion network-transition entropy (DNTE), which integrates the concepts of complex networks and information entropy. Specifically, we assign single cumulative distribution function values to network nodes and utilize Markov chains to represent links, transforming nonlinear signals into weighted directed complex networks. Subsequently, we assess the importance of network nodes and links, and employ the mathematical expression of information entropy to calculate the DNTE value, quantifying the complexity of the original signal. Next, through extensive experiments on simulated chaotic models and real underwater acoustic signals, we confirm the outstanding performance of DNTE. The results indicate that, compared to Lempel-Ziv complexity, permutation entropy, and dispersion entropy, DNTE not only more accurately reflects changes in signal complexity but also exhibits higher computational efficiency. Importantly, DNTE demonstrates optimal performance in distinguishing different categories of chaotic models, ships, and modulation signals, showcasing its significant potential in extracting effective information from signals.

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

Influence Maximization Based on Adaptive Graph Convolution Neural Network in Social Networks

The influence maximization problem is a hot issue in the research on social networks due to its wide application. The problem aims to find a small subset of influential nodes to maximize the influence spread. To tackle the challenge of striking a balance between efficiency and effectiveness in traditional influence maximization algorithms, deep learning-based influence maximization algorithms have been introduced and have achieved advancement. However, these algorithms still encounter two key problems: (1) Traditional deep learning models are not well-equipped to capture the latent topological information of networks with varying sizes and structures. (2) Many deep learning-based methods use the influence spread of individual nodes as labels to train a model, which can result in an overlap of influence among the seed nodes selected by the model. In this paper, we reframe the influence maximization problem as a regression task and introduce an innovative approach to influence maximization. The method adopts an adaptive graph convolution neural network which can explore the latent topology information of the network and can greatly improve the performance of the algorithm. In our approach, firstly, we integrate several network-level attributes and some centrality metrics into a vector as the presentation vector of nodes in the social network. Next, we propose a new label generation method to measure the influence of nodes by neighborhood discount strategy, which takes full account of the influence overlapping problem. Subsequently, labels and presentation vectors are fed into an adaptive graph convolution neural network model. Finally, we use the well-trained model to predict the importance of nodes and select top-K nodes as a seed set. Abundant experiments conducted on various real-world datasets have confirmed that the performance of our proposed algorithm surpasses that of several current state-of-the-art algorithms.

Flexible load dispatching strategy for substation considering time-of-use price

Abstract The substation is an important node in the power system. Reducing the operating costs and carbon emissions of substations is of great significance for power enterprises to reduce costs and carbon emissions. Meanwhile, with the development of new substations, more substations have added rooftop photovoltaic systems to reduce carbon emissions. How to improve the utilization rate of rooftop photovoltaic power generation has also become a key focus of substation development. This article establishes a flexible load optimization scheduling model for substations, and based on the consideration of system costs, sets up two scenarios for comparison. The research results indicate that the flexible load optimization scheduling model for substations can significantly reduce the operational carbon emissions and costs caused by electricity consumption within the substation.

Towards Inductive and Efficient Explanations for Graph Neural Networks.

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging and nascent problem. The leading method mainly considers the local explanations, i.e., important subgraph structure and node features, to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized at the instance level. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to the lack of generalizability and hindering it from being used in the inductive setting. Besides, training the explanation model explaining for each instance is time-consuming for large-scale real-life datasets. In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which renders PGExplainer a natural approach to multi-instance explanations. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting without training the model for new instances. Thus, PGExplainer is much more efficient than the leading method with significant speed-up. In addition, the explanation networks can also be utilized as a regularizer to improve the generalization power of existing GNNs when jointly trained with downstream tasks. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification over the leading baseline.

Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs

Node ranking in heterogeneous graphs, which quantifies the relative importance of nodes, can often be improved by incorporating information from relevant paths. Graph database and heterogeneous graph neural network (HGNN) are two main approaches to better solve this problem. Graph databases support efficient path queries for flexible path types but require manual design to combine results for node ranking. Conversely, current HGNNs can automatically integrate semantic information from multiple linear path types for accurate node ranking. However, our experiments show that they fail to outperform a multi-layer perceptron model that utilizes features extracted from multiple nonlinear conditional paths, which can be handled by graph databases. Therefore, we aim to enable HGNN to take advantage of these path types for better performance. However, HGNNs require a generalized path schema to define the structure of input paths, and incorporating each additional path type will significantly increase the required system memory and sampling time for HGNNs. To address these limitations, we introduce CompNode, a novel framework based on a new unified path schema definition called Complex-path, which is used to describe all the required path types, including nonlinear conditional path types. Then, we design a pre-aggregation method to reduce the required system memory and sampling time by pre-aggregating the same type of complex-path. Furthermore, we develop a model that combines semantic information from all aggregated complex-paths for accurate node ranking. Real-world experiments on identifying top potential high-value customers show CompNode outperforms state-of-the-art HGNNs by 20% in average precision and the previously deployed graph database method by 252% in success rate.

A Network Approach to Understanding the Role of Executive Functioning and Alpha Oscillations in Inattention and Hyperactivity-Impulsivity Symptoms of ADHD.

ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, "seat" (difficulty staying seated), and "runs" (running or climbing excessively). EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Aviation armament system-of-systems modeling and identification method of vulnerable nodes based on interdependent network

Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems (SoS) due to complex equipment interaction relationships and high confrontation, the interdependent network theory is introduced to solve it. Firstly, a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed, which reflects the information interaction, functional dependency and inter-network dependence effectively. Secondly, using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point, the node importance index and the attack cost measurement method are constructed. Thirdly, the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection, and the vulnerable node identification method based on tabu search is proposed. Based on vulnerable nodes, a robustness enhancement strategy for aviation armament SoS network is presented. Finally, the above methods are used to an aerial confrontation SoS, and the results verify the rationality and effectiveness of the proposed methods.

Research on the Application of Node Importance Assessment based on HITs Algorithm in Power Grid Planning

Research on the Application of Node Importance Assessment based on HITs Algorithm in Power Grid Planning

Arquitetura moderna para a ditadura brasileira: Ambivalência no Quartel Militar de São Paulo, Parque do Ibirapuera (1965)

This article examines the relationship between modern architecture and the Brazilian Military Dictatorship (1964-1985) in the case of the military headquarters designed by communist architects for a violently anti-communist regime: the Second Army Headquarters in Ibirapuera, a project by a team led by the architect Paulo Bastos, a case with strong symbolic entanglements in the heart of São Paulo. Although the architects were against the regime and were the target of the dictatorship’s repression, a close look at this case reveals nuances in the actors' reactions to that context, as well as a more complex relationship between architecture and authoritarianism, which goes beyond the binary lenses of resistance or collaboration. This case is an important node to reflect on the complex relationships between architecture and politics, especially under authoritarian regimes. It also helps to reflect on modern architecture itself, the immanent contradictions of its objects, and the ambivalences of the epistemological investments that underpin it.

New Random Walk Algorithm Based on Different Seed Nodes for Community Detection

A complex network is an abstract modeling of complex systems in the real world, which plays an important role in analyzing the function of complex systems. Community detection is an important tool for analyzing network structure. In this paper, we propose a new community detection algorithm (RWBS) based on different seed nodes which aims to understand the community structure of the network, which provides a new idea for the allocation of resources in the network. RWBS provides a new centrality metric (MC) to calculate node importance, which calculates the ranking of nodes as seed nodes. Furthermore, two algorithms are proposed for determining seed nodes on networks with and without ground truth, respectively. We set the number of steps for the random walk to six according to the six degrees of separation theory to reduce the running time of the algorithm. Since some traditional community detection algorithms may detect smaller communities, e.g., two nodes become one community, this may make the resource allocation unreasonable. Therefore, modularity (Q) is chosen as the optimization function to combine communities, which can improve the quality of detected communities. Final experimental results on real-world and synthetic networks show that the RWBS algorithm can effectively detect communities.

Identifying highly connected sites for risk-based surveillance and control of cucurbit downy mildew in the eastern United States.

Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread. Static networks were constructed based on the distance between fields, while dynamic networks were constructed based on the distance between fields and wind speed and direction, using disease data collected from 2008 to 2016. Three strategies were used to identify highly connected field sites. First, the probability of pathogen transmission between nodes and the probability of node infection were modeled over a discrete weekly time step within an epidemic year. Second, nodes identified as important were selectively removed from networks and the probability of node infection was recalculated in each epidemic year. Third, the recurring patterns of node infection were analyzed across epidemic years. Static networks exhibited scale-free properties where the node degree followed a power-law distribution. Betweenness centrality was the most useful metric for identifying important nodes within the networks that were associated with disease transmission and prediction. Based on betweenness centrality, field sites in Maryland, North Carolina, Ohio, South Carolina and Virginia were the most central in the disease network across epidemic years. Removing field sites identified as important limited the predicted risk of disease spread based on the dynamic network model. Combining the dynamic network model and centrality metrics facilitated the identification of highly connected fields in the southeastern United States and the mid-Atlantic region. These highly connected sites may be used to inform surveillance and strategies for controlling cucurbit downy mildew in the eastern United States.

Chemical burn wounds as a risk factor for gastric cancer: in-silico analyses-experimental research.

The present study employs bioinformatics tools to identify shared upregulated genes between chemical burns and gastric cancer. Gene Expression Omnibus (GEO) retrieved gene sets for this investigation. GSEs with P value less than 0.05 and LOG fold change (FC) greater than 1 were valid and upregulated. Gastric cancer and chemical burn common elevated genes were found using Venn diagram online tools. In the second stage, the "string" visualized gastric cancer elevated genes network, and non-coding RNAs were deleted, and "interaction" greater than 1 was examined to choose important gene nodes. Next, they explored the String gene-interaction network for common genes. To determine the most interacting genes, Gephi (V 0.9.7) used "betweenness centrality" greater than "0" to evaluate the twenty-gene network. TISIDB and drug banks provide gene-related medications. In the present study, two genes, including ALOX5AP and SERPINB2, were obtained, with the highest centrality among chemical burns and gastric cancer shared genes. Additionally, the current study presented five drugs, including Urokinase, Tenecteplase, DG031, AM103, and Fiboflapon, which can have predicted effects on gastric cancer following chemical burns. According to current in-silicon analyses, ALOX5AP and SERPINB2 are linked genetic keys between gastric chemical burn and cancer. Considering that burn is an environmental factor that leads to the upregulation of the two genes thus, the chemical burn can be related to the incidence of gastric cancer.

Optimization and evaluation of modified release solid dosage forms using artificial neural network

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

Evaluation of the benefit of indocyanine green as an educational and practical tool for ureteral identification in laparoscopic pelvic surgery: a cross-sectional study

Background Indocyanine green (ICG) is a visible near-infrared fluorescent dye. Several studies have reported its benefit in identifying important anatomical structures, tissue vascularization, and sentinel lymph nodes in the case of tumors. Studies have shown that ICG is critical and safe in gynecologic surgeries. However, research on how ICG dye can help surgeons in laparoscopic surgeries correctly identify the course of the ureter has yet to be further investigated. Method This cross-sectional study enrolled 62 gynecology attending and resident surgeons who were asked to identify the course of the ureter on images of laparoscopic surgeries. The results were then compared with images in which ICG dye highlighted the course of the ureter. The purpose of this study was to detect the ability of surgical assistants and residents to adequately identify the course of the ureter in laparoscopic pelvic surgeries. Results No statistically significant differences were found in terms of year of residency, years of experience, number of laparoscopic procedures attended, and correct identification of ureter course. ICG proved useful in identifying the correct ureteral trajectory. Conclusions ICG can be a valuable tool to improve the correct identification of ureters and improve surgical outcomes.

A Key Node Mining Method Based on K-Shell and Neighborhood Information

Mining key nodes in complex networks has always been a promising research direction in the field of complex networks. Many precise methods proposed by researchers for mining influential special nodes in networks have been widely applied in a plethora of fields. However, some important node-mining methods often use the degree as a node attribute indicator for evaluating node importance, while the clustering coefficient, as an important attribute of nodes, is rarely utilized. Some methods only consider the global position of nodes in the network while ignoring the local structural information of nodes in special positions and the network. Hence, this paper introduces a novel node centrality method, KCH. The KCH method leverages K-shell to identify the global position of nodes and assists in evaluating the importance of nodes by combining information such as structural holes and local clustering coefficients of first-order neighborhoods. This integrated approach yields an enhanced performance compared to existing methods. We conducted experiments on connectivity, monotonicity, and zero models on 10 networks to evaluate the performance of KCH. The experiments revealed that when compared to the collective influence baseline methods, such as social capital and hierarchical K-shell, the KCH method exhibited superior capabilities in terms of collective influence.

Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

BackgroundIn gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases.ResultsThe ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier.ConclusionsThe identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.

Simulation Study on Sea-Rail Intermodal Diversion Operation of Container Terminal in the Beilun Port Area of Ningbo Zhoushan Port

With the continuous development of global trade, the Beilun port area of Ningbo Zhoushan Port, as an important node of international shipping, its efficient and orderly container dredging operation is crucial to ensure the overall operational efficiency of the port and the stability of the regional supply chain. However, the actual operation is faced with multiple constraints such as fluctuating berth utilization rate, complex truck scheduling, tight yard space, and so on, which need to be optimized by scientific means. To achieve more efficient container transport, it is necessary to further optimize and improve sea-rail intermodal transport to improve the efficiency of port container operations. Therefore, this paper is based on the background of the rapid development of sea-rail intermodal transport of port containers, focusing on the problem of dredging operation of container terminals in the Beilun port area of Ningbo Zhoushan Port, through the analysis of the characteristics and influencing factors of container dredging operation, research on the optimization of container dredging scheme and put forward optimization scheme for the problem of dredging in Beilun Port Area of Ningbo Zhoushan Port, which is aimed at improving the efficiency and economy of container dredging operation and the efficiency of container dredging operation in Beilun port area through the advanced modeling and simulation technology. Through advanced modeling and simulation technology, it aims to effectively improve the efficiency and economy of container dredging operations in the Beilun port area, to cope with the increasing throughput pressure and the challenges of a complex logistics environment, and to provide theoretical support and practical guidance for the development of China’s sea-railway intermodal transport.

Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study

IntroductionFrailty has a significant impact on the overall quality of life of older cancer survivors, but the relationships among frailty symptoms are not well understood. This study aims to explore the specific associations among multidimensional symptoms of frailty among older cancer survivors by employing network analysis to provide supportive evidence for targeted interventions in the future. Materials and MethodsData were obtained by cluster sampling from three large Grade-A tertiary hospitals in Shandong Province, China, and collected through face-to-face interviews by trained investigators. We included patients who were diagnosed with a solid malignant tumor at the age of 60 years or older. Frailty indicators were measured by the Groningen Frailty Indicator (GFI) and analyzed primarily through network analysis, including network estimation, centrality, and stability analysis. The relative importance of a node in a network was tested by centrality analyses, and Spearman correlations were applied to estimate the relationships between symptom pairs (symptom score) and symptom clusters (standardized symptom score) in the symptom network. In terms of centrality, the indexes of strength, closeness, and betweenness were adopted to measure the importance of nodes. ResultsFive hundred and eight older cancer survivors were included, with an average age of 68.4 years (standard deviation [SD] = 5.4), and a higher proportion were male (n = 307[60.4%]). The prevalence of frailty among older cancer survivors was 58.9% (n = 299), with a mean GFI score of 4.46 (SD = 2.87). The strongest edge was between “dressing and undressing” and “going to the toilet” (r = 0.58). The nodes with the higher strength centrality were “going to the toilet” (rS=1.09), “walking around outside” (rS=0.97), and “part of social network” (rS=0.96); and the nodes with the higher closeness centrality were “mark physical fitness” (rC=0.005), “calm and relaxed” (rC=0.005), and “nervous or downhearted” (rC=0.005). DiscussionThis study demonstrated that older cancer survivors in China have a high prevalence of frailty, with self-care and social participation-related symptoms playing a key role in the multidimensional network of frailty symptoms. Psychological symptoms can rapidly influence other symptoms within this network. Therefore, prioritizing psychological symptoms in the assessment of older adults with cancer is essential for effective frailty management.

Identification of Key Risk Nodes and Invulnerability Analysis of Construction Supply Chain Networks

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.