Importance Of Nodes In Network Research Articles

Exploring the Influence from London Development on London Underground Mobility Through Network Analysis

As cities grows up, people continue to gather in cities, and human activities in cities have become more intense than ever before. These human activities will subtly reshape urban space, causing significant transformation of the urban physical environment and socio-economic environment. The method of big data provides a promising perspective for us to explore the impact of human activities on the urban environment. This study investigates the commuting patterns of London citizens in the subway and explores the impact of human activities on the importance of network nodes in subway stations. We use network analysis to characterize the node’s importance through degree, closeness, and betweenness centrality, and then compare these centralities with the centrality with the human mobility as weight. The result shows that the node’s importance based on centrality characteristics are largely reshaped by human mobility, which in turn drive the government to build new lines to hold the mobility flow. This research is helpful for providing insights for the London subway and urban construction.

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.

Consensus reaching with the dynamic hybrid trust network in group decision making based on an extensive bounded confidence

This study focuses on the consensus-reaching process (CRP) in social networks. Over the years, ample research has been conducted on CRP levels. However, most feedback mechanisms do not consider the decision maker’s (DM) willingness to accept the recommendations provided by these mechanisms. Therefore, this study proposes an extensive bounded confidence-based group consensus feedback mechanism that primarily consists of two parts: (a) the construction of a hybrid dynamic trust network and (b) the construction of an extensive bounded confidence model. In the first part, we propose an algorithm to calculate the importance of network nodes and apply it to the trust propagation and aggregation process. In the second part, we propose an extensive bounded confidence model to address the limitations of existing traditional bounded confidence models. In the proposed extensive bounded confidence model, the trust threshold is no longer a “switch” to determine whether a DM interacts with other DMs but a basis to determine the strength of the DMs’ interaction; moreover, the hybrid dynamic trust relationship will determine the negotiation sets. A practical case of site selection and comparative analysis verify the effectiveness and flexibility of the proposed mechanism.

Cause Analysis of Elevator Accidents Based on Complex Network

In order to excavate the key causes of elevator accidents, 70 elevator accident investigation reports from 2018 to 2023 were collected, and data mining was carried out to extract "4M" unsafe factors. On this basis, based on the complex network, the model of elevator accident causation network is constructed, and the statistical characteristics of this model are analyzed. Finally, the entropy weight-TOPSIS is used to construct a comprehensive evaluation model of network node importance, which comprehensively evaluates the causes of elevator accidents from four indicators, including degree centrality, intermediary centrality, proximity centrality and aggregation coefficient of nodes, and identifies the key factors of elevator accidents. The results show that unsafe factors such as defects of equipment and facilities, illegal operation, improper operation, weak safety awareness, inadequate implementation of safety responsibilities, and inadequate on-site supervision are the key causes of elevator accidents, which need to be controlled and controlled.

Random Walks-Based Node Centralities to Attack Complex Networks

Investigating the network response to node removal and the efficacy of the node removal strategies is fundamental to network science. Different research studies have proposed many node centralities based on the network structure for ranking nodes to remove. The random walk (RW) on networks describes a stochastic process in which a walker travels among nodes. RW can be a model of transport, diffusion, and search on networks and is an essential tool for studying the importance of network nodes. In this manuscript, we propose four new measures of node centrality based on RW. Then, we compare the efficacy of the new RW node centralities for network dismantling with effective node removal strategies from the literature, namely betweenness, closeness, degree, and k-shell node removal, for synthetic and real-world networks. We evaluate the dismantling of the network by using the size of the largest connected component (LCC). We find that the degree nodes attack is the best strategy overall, and the new node removal strategies based on RW show the highest efficacy in regard to peculiar network topology. Specifically, RW strategy based on covering time emerges as the most effective strategy for a synthetic lattice network and a real-world road network. Our results may help researchers select the best node attack strategies in a specific network class and build more robust network structures.

Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks

Identifying the most influential spreaders is important to understand and control the spreading process in a network. As many real-world complex systems can be modeled as multilayer networks, the question of identifying important nodes in multilayer network has attracted much attention. Existing studies focus on the multilayer network structure, while neglecting how the structural and dynamical coupling of multiple layers influence the dynamical importance of nodes in the network. Here we investigate on this question in an information-disease coupled spreading dynamics on multiplex networks. Firstly, we explicitly reveal that three interlayer coupling factors, which are the two-layer relative spreading speed, the interlayer coupling strength and the two-layer degree correlation, significantly impact the spreading influence of a node on the contact layer. The suppression effect from the information layer makes the structural centrality on the contact layer fail to predict the spreading influence of nodes in the multiplex network. Then by mapping the coevolving spreading dynamics into percolation process and using the message-passing approach, we propose a method to calculate the size of the disease outbreaks from a single seed node, which can be used to estimate the nodes' spreading influence in the coevolving dynamics. Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.

Hybrid weighted communication network node importance evaluation method

Communication networks are used as an important guarantee for information interaction and efficient collaboration within many fields and systems; however, under information technology conditions, the destruction of a number of nodes in a network may have a great impact on the overall operation of the network. Therefore, it is important to accurately determine the critical nodes in the network to enhance the network’s resistance to destruction. Combining the characteristic attributes of the communication network, a node contribution evaluation matrix is proposed based on the efficiency matrix, from the perspective of node receiving information; a node value evaluation matrix is proposed from the perspective of a node providing information to neighboring nodes, and node importance is calculated by integrating the evaluation results of the two matrices and the node’s own attributes. The algorithm is suitable for directed-weighted network node value evaluation, and the effectiveness and accuracy of the algorithm are verified by comparing other algorithms for a small-scale network. In further experimental validation, a hybrid weighted communication network evolution model based on organizational structured networks is proposed, and networks of different sizes are generated for experimental simulation. The results show that when nodes with high importance are removed from the network, they can cause a rapid decrease in the network efficiency and maximum connectivity, confirming the accuracy of the algorithm in evaluating the importance of nodes and identifying critical nodes in the network.

A multiple features fusion-based social network node importance measure for rumor control

A multiple features fusion-based social network node importance measure for rumor control

Identifying the influential nodes in complex social networks using centrality-based approach

Estimating the importance of nodes in complex social networks is significant for understanding the robustness and stability of a network such as preventing the spread of disease, rumors, or power grids from shutting down. Existing relevant literature presents diverse network centrality measures for quantifying a node’s importance, however, each of the centrality measures is based on a single criterion having its own limitations. This study introduces a novel centrality model that is based on a combination of existing centrality measures for ranking important nodes in complex social networks. The study employs the entropy weighting technique for assigning objective weights to each single criterion, and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is utilized for ranking the importance of a node in the network. For a comprehensive empirical analysis, four real-world social networks are utilized for evaluating the effectiveness of the proposed model against the existing methods. The results of the study confirm the superiority and effectiveness of the proposed model in ranking the importance of network nodes as compared to a single centrality criterion.

CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy

How to evaluate the importance of nodes and identify influential nodes in complex networks is a very significant research in the field of network science. Most of existing algorithms neglect the relationship between a node and its neighbors to evaluate the importance of nodes in networks. In this work, we first define nodes communication probability sequence by making use of the length and number of shortest paths between node pairs. Then the traditional binary adjacency matrix is converted into correlation matrix through relative entropy. Based on information Communication Probability and Relative entropy (CPR), an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), called CPR-TOPSIS, is presented for identifying influential nodes in complex networks from the view of global, local and location information dimensions. The proposed algorithm has been compared with eight state-of-the-art algorithms on several real-world networks to verify the performance. Experimental results show that CPR-TOPSIS has better performance in terms of monotonicity, resolution, ranking accuracy, imprecision function and top-10 nodes.

Importance evaluation of power network nodes based on community division and characteristics of coupled network

The identification of important nodes in the power and communication coupling network is beneficial to prevent the occurrence of cascading failures. It is of great significance to establish a robust and resilient coupling network. This paper proposes a new method for evaluating the importance of power information network nodes based on link-based community division and Betweenness Centrality Based on Neighbor Nodes (BCBNN) algorithm to prevent better power grid cascading failures. This method can evaluate the importance of nodes in the network, except the nodes whose node betweenness is zero. First of all, this article uses the link-based dividing method explores the overlapping communities in the power grid; secondly, it calculates the importance of nodes in a single-layer network based on BCBNN algorithm; finally, it proposes a characteristic node importance assessment method considering single-layer network topology, grid overlapping structure and dual-network coupling. In addition, this paper has conducted experiments on the standard IEEE30-node system and improved IEEE30-node system. Compared with the node shrinkage method and Max-Cas algorithm, the experimental results not only identify traditional nodes, but also mines potential nodes that could cause the grid to fail. It means that the proposed algorithm has a better degree of differentiation in terms of importance assessment.

Efficient Computation of Betweenness Centrality by Graph Decompositions and Their Applications to Real-World Networks

Betweenness centrality is one of the most significant and commonly used centralities, where centrality is a notion of measuring the importance of nodes in networks. In 2001, Brandes proposed an algorithm for computing betweenness centrality efficiently, and it can compute those values for all nodes in O(nm) time for unweighted networks, where n and m denote the number of nodes and links in networks, respectively. However, even Brandes' algorithm is not fast enough for recent large-scale real-world networks, and therefore, much faster algorithms are expected. The objective of this research is to theoretically improve the efficiency of Brandes' algorithm by introducing graph decompositions, and to verify the practical effectiveness of our approaches by implementing them as computer programs and by applying them to various kinds of real-world networks. A series of computational experiments shows that our proposed algorithms run several times faster than the original Brandes' algorithm, which are guaranteed by theoretical analyses.

Dynamic importance of network nodes is poorly predicted by static structural features

One of the most central questions in network science is: which nodes are most important? Often this question is answered using structural properties such as high connectedness or centrality in the network. However, static structural connectedness does not necessarily translate to dynamical importance. To demonstrate this, we simulate the kinetic Ising spin model on generated networks and one real-world weighted network. The dynamic impact of nodes is assessed by causally intervening on node state probabilities and measuring the effect on the systemic dynamics. The results show that structural features such as network centrality or connectedness are actually poor predictors of the dynamical impact of a node on the rest of the network. A solution is offered in the form of an information theoretical measure named integrated mutual information. The metric is able to accurately predict the dynamically most important node (“driver” node) in networks based on observational data of non-intervened dynamics. We conclude that the driver node(s) in networks are not necessarily the most well-connected or central nodes. Indeed, the common assumption of network structural features being proportional to dynamical importance is false. Consequently, great care should be taken when deriving dynamical importance from network data alone. These results highlight the need for novel inference methods that take both structure and dynamics into account.

Importance of Agents in Networks: Clique Based Game-Theoretic Approach

Centrality measures are commonly used to detect important nodes. There are some metrics that measure a node's connectivity to different communities. This paper extends the standard network centrality measures and proposes to estimate the importance of nodes in network as a solution of a cooperative game. Three ways of defining such cooperative game are introduced. Each of them uses the concept of a clique in graph. Examples are considered

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

Continuous metro-operation accidents lead to serious economic loss and a negative social impact. The accident causation analysis is of great significance for accident prevention and metro operation safety promotion. Network node importance (NNI) evaluation has been widely used as a tool for ranking the nodes in complex networks; however, traditional indicators such as degree centrality (DC) are insufficient for examining accident networks. This study proposed an improved method by integrating decision making trail and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) into traditional NNI evaluation, where the key nodes are determined by both the nature of the accident network topology and the contribution of the nodes to accident development. Drawing on this method, 32 accident causal factors were identified and prioritized on the ground of 248 accident cases. It was found that 14 important factors related to staff (e.g., “driver noncompliance”), environment (e.g., “extrinsic nature disturbance”), passenger (e.g., “passenger sudden illness”), and machine (e.g., “track failures”) should be given priority in safety management due to their significant tendency of causing metro accidents. Theoretical and managerial implications were discussed to provide useful insights into the understanding of the causation of metro accidents and form a basis for metro managers to develop targeted safety countermeasures related to metro operation. The proposed hybrid method is proven effective in investigating accident networks involving sequential and casual relationships and revealing factors with high possibility to increase accidents.

METHOD FOR IDENTIFYING IMPORTANT NODES IN THE CITATION NETWORK OF SCIENTIFIC PUBLICATIONS

A mathematical method for selecting and ranking publications according to the degree of their compliance with the objectives of the research is developed and investigated. Bibliographic and abstract databases are used as primary sources of data on publications, which make it possible to track the citation of publications and identify the corresponding citation networks. The subject of the study is the citation networks of scientific publications. The mathematical model of citation networks is simple directed graphs whose vertices correspond to publications and whose arcs correspond to bibliographic references. The objectives of the research can be the preparation of a scientific article, writing a monograph or textbook, the design of a final qualifying work or dissertation, the replenishment of the library fund, etc. The research is carried out using the methods of Network Science. A method is proposed for determining the importance of the nodes of the citation network of scientific publications, taking into account the relevant measures of the centrality of the nodes and the profile of the research of publications. Relevant measures of centrality are indicators of the importance of nodes that are more appropriate than others and meet the search query for the selection of publications. The paper considers three profiles of the research of the citation network in order to rank publications. Since the citation network is oriented, the incoming and outgoing connections of the node are analyzed separately. The difference between the study profiles is that in one of them, only outgoing connections are taken into account in the centrality measures, in the other – incoming connections, and in the next, both are taken into account. An example of the application of the developed method of selection and ranking of scientific publications is given. The experimental values of the network node importance indicators were obtained on the basis of data on the citation of scientific publications in the RePEc bibliographic database.

A community detection algorithm based on Quasi-Laplacian centrality peaks clustering

Searching for key nodes in social networks and clustering communities are indispensable components in community detection methods. With the wide application demand of detecting community networks, more and more algorithms have been proposed. Laplacian centrality peaks clustering (LPC) is an efficient and simple algorithm which is proposed on the basis of density peaks clustering (DPC) to identify clusters without parameters and prior knowledge. Before LPC is widely applied in community detection algorithms, some shortcomings should be addressed. Firstly, LPC fails to search for key nodes in networks accurately because of the similarity calculation method. Secondly, it takes too much time for LPC to calculate the Laplacian centrality of each point. To address these issues, a community detection algorithm based on Quasi-Laplacian centrality peaks clustering (CD-QLPC) is proposed after studying the advantages of Quasi-Laplacian centrality which can replace density or Laplacian centrality to characterize the importance of nodes in networks. Quasi-Laplacian centrality is obtained by the degree of each node directly, which needs less time than Laplacian centrality. In addition, a trust-based function is utilized to obtain the similarity accurately. Moreover, a new modularity-based merging strategy is adopted to identify the optimal number of communities adaptively. Experimental results show that CD-QLPC outperforms many state-of-the-art methods on both real-world networks and synthetic networks.

Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect

In recent years, the increasing segregation and fragmentation of transport service provisions, evidenced between urban and rural areas, and between different administrative divisions, has been concerned in metropolitan areas. The analysis of the spatial configuration of urban bus networks is important to understand the spatial problems and relevant mitigation solutions. Previous studies have regarded the station as the nodes in bus network analyses, while few studies have constructed bus lines as nodes through the complex network analysis. In this study, we highlighted the value of bus lines and their network types for analyzing urban bus network pattern. We developed a novel ‘line–line’ network to examine the spatial characteristics of cross-administration bus lines in a case study of Nanjing metropolitan area, China. The results suggest that the bus network exhibited a consistently bimodal node distribution in both line-line and line-node network types, though their internal mechanisms are different. By evaluating the importance of network nodes, administrative divisions were found to impact the bus network integration in the metropolitan area negatively. Community detection results further revealed that the geography of some subnetworks was affected by administrative divisions, with some exhibiting complex spatial characteristics. These insights would inform strategic development policy to improve the spatial equity and efficiency of public transport provisions in urban China.

Research on D-GERT Model of Joint Operational Situation Assessment Based on Information Flow

To solve the problem of inaccurate description of battlefield information changes in the process of joint system operations, this paper constructed a joint operational situation assessment D-GERT network model based on information flow, on the basis of the GERT network. And the calculation method of the threat degree, the increment of battlefield information value and the importance of network nodes were given. Finally, MATLAB was used to simulate and verify the model. The results showed that the method in this paper conforms to objective reality and can provide good method support for the commander's combat decision-making.

A survey on network node ranking algorithms: Representative methods, extensions, and applications

The ranking of network node importance is one of the most essential problems in the field of network science. Node ranking algorithms serve as an essential part in many application scenarios such as search engine, social networks, and recommendation systems. This paper presents a systematic review on three representative methods: node ranking based on centralities, PageRank algorithm, and HITS algorithm. Furthermore, we investigate the latest extensions and improvements of these representative methods, provided with several main application fields. Inspired by the survey of current literatures, we attempt to propose promising directions for future research. The conclusions of this paper are enlightening and beneficial to both the academic and industrial communities.