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Journal articles on the topic 'New Weighted Complex Network'

Author: Grafiati

Published: 10 January 2023

Last updated: 31 July 2025

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1

WANG, XUTAO, HONGTAO LU, and GUANRONG CHEN. "THE MODELLING OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 16 (2007): 2813–20. http://dx.doi.org/10.1142/s0217979207037399.

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In order to further explore the mechanism responsible for weighted complex networks, we introduce a new model that incorporates the network topology and the weights' dynamical evolutions. Our model can capture the details of weight dynamics caused not only by the addition of a new node with new links and new links between old nodes, but also the deletion of old links. We calculate analytically the distributions of both degree and strength and found that all these distributions show scale-free behavior, as confirmed in many real networks. Thus our model characterizes the real weighted complex n

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Nguyen, Quang, Ngoc-Kim-Khanh Nguyen, Davide Cassi, and Michele Bellingeri. "New Betweenness Centrality Node Attack Strategies for Real-World Complex Weighted Networks." Complexity 2021 (October 15, 2021): 1–17. http://dx.doi.org/10.1155/2021/1677445.

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In this work, we introduce a new node attack strategy removing nodes with the highest conditional weighted betweenness centrality (CondWBet), which combines the weighted structure of the network and the node’s conditional betweenness. We compare its efficacy with well-known attack strategies from literature over five real-world complex weighted networks. We use the network weighted efficiency (WEFF) like a measure encompassing the weighted structure of the network, in addition to the commonly used binary-topological measure, i.e., the largest connected cluster (LCC). We find that if the measur

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3

TASGIN, MURSEL, and HALUK O. BINGOL. "GOSSIP ON WEIGHTED NETWORKS." Advances in Complex Systems 15, supp01 (2012): 1250061. http://dx.doi.org/10.1142/s0219525912500610.

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In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spread

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Xing, Yingying, Jian Lu, and Shendi Chen. "Weighted Complex Network Analysis of Shanghai Rail Transit System." Discrete Dynamics in Nature and Society 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1290138.

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With increasing passenger flows and construction scale, Shanghai rail transit system (RTS) has entered a new era of networking operation. In addition, the structure and properties of the RTS network have great implications for urban traffic planning, design, and management. Thus, it is necessary to acquire their network properties and impacts. In this paper, the Shanghai RTS, as well as passenger flows, will be investigated by using complex network theory. Both the topological and dynamic properties of the RTS network are analyzed and the largest connected cluster is introduced to assess the r

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Wang, Yu, Jinli Guo, and Han Liu. "A New Evaluation Method of Node Importance in Directed Weighted Complex Networks." Journal of Systems Science and Information 5, no. 4 (2017): 367–75. http://dx.doi.org/10.21078/jssi-2017-367-09.

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AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper appli

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Wu, Huijun, Hao Wang, and Linyuan Lü. "Individual T1-weighted/T2-weighted ratio brain networks: Small-worldness, hubs and modular organization." International Journal of Modern Physics C 29, no. 05 (2018): 1840007. http://dx.doi.org/10.1142/s0129183118400077.

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Applying network science to investigate the complex systems has become a hot topic. In neuroscience, understanding the architectures of complex brain networks was a vital issue. An enormous amount of evidence had supported the brain was cost/efficiency trade-off with small-worldness, hubness and modular organization through the functional MRI and structural MRI investigations. However, the T1-weighted/T2-weighted (T1w/T2w) ratio brain networks were mostly unexplored. Here, we utilized a KL divergence-based method to construct large-scale individual T1w/T2w ratio brain networks and investigated

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Malekzadeh, Milad, and Jed A. Long. "A network community structure similarity index for weighted networks." PLOS ONE 18, no. 11 (2023): e0292018. http://dx.doi.org/10.1371/journal.pone.0292018.

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Identification of communities in complex systems is an essential part of network analysis. Accordingly, measuring similarities between communities is a fundamental part of analysing community structure in different, yet related, networks. Commonly used methods for quantifying network community similarity fail to consider the effects of edge weights. Existing methods remain limited when the two networks being compared have different numbers of nodes. In this study, we address these issues by proposing a novel network community structure similarity index (NCSSI) based on the edit distance concep

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8

An, Xin-lei, and Li Zhang. "A New Complex Network Model with Multiweights and Its Synchronization Control." Advances in Mathematical Physics 2020 (January 13, 2020): 1–12. http://dx.doi.org/10.1155/2020/2872795.

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Based on the weighted complex network model, this paper establishes a multiweight complex network model, which possesses several different weights on the one edge. According to the method of network split, the complex network with multiweights is split into several different complex networks with single weight. Some new static characteristics, such as node weight, node degree, node weight strength, node weight distribution, edge weight distribution, and diversity of weight distribution are defined. Then, by using Lyapunov stability theory, the adaptive feedback synchronization controller is de

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DAI, MEIFENG, and DANPING ZHANG. "A WEIGHTED EVOLVING NETWORK WITH AGING-NODE-DELETING AND LOCAL REARRANGEMENTS OF WEIGHTS." International Journal of Modern Physics C 25, no. 02 (2014): 1350093. http://dx.doi.org/10.1142/s0129183113500939.

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In previous study of complex network, researchers generally considered the increase of the un-weighted network by the method of adding new nodes and new links. However, most of real networks are weighted and characterized by capacities or strength instead of a binary state (present or absent), and their nodes and links experience both increase and deletion. Barrat, Barthlemy and Vespignani, Phys. Rev. Lett.92, 228701 (2004) presented an evolutionary model (BBV model) to investigate weighted networks. We present a weighted evolution network model based on BBV model, which not only considers to

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Luo, Yutai, Baocheng Sha, and Tao Xu. "A Recommended Method Based on the Weighted RippleNet Network Mode." Journal of Physics: Conference Series 2025, no. 1 (2021): 012011. http://dx.doi.org/10.1088/1742-6596/2025/1/012011.

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Abstract User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the

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Koam, Ali N. A., Muhammad Faisal Nadeem, Ali Ahmad, and Hassan A. Eshaq. "Weighted Asymmetry Index: A New Graph-Theoretic Measure for Network Analysis and Optimization." Mathematics 12, no. 21 (2024): 3397. http://dx.doi.org/10.3390/math12213397.

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Graph theory is a crucial branch of mathematics in fields like network analysis, molecular chemistry, and computer science, where it models complex relationships and structures. Many indices are used to capture the specific nuances in these structures. In this paper, we propose a new index, the weighted asymmetry index, a graph-theoretic metric quantifying the asymmetry in a network using the distances of the vertices connected by an edge. This index measures how uneven the distances from each vertex to the rest of the graph are when considering the contribution of each edge. We show how the i

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Chen, Xin Yi. "Priority Weighted Fitness Model in Networks." Applied Mechanics and Materials 229-231 (November 2012): 1854–57. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1854.

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Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with priority selected different edges of weighted selected that connected to different vertices in the system, and (iii) by the fitness probability that a new vertices attach preferentially to sites that are already well c

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Alodjants, A. P., A. Yu. Bazhenov, and M. M. Nikitina. "Phase Transitions in Quantum Complex Networks." Journal of Physics: Conference Series 2249, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2249/1/012014.

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Abstract In this work we examine a superradiant (SR) and/or ferromagnetic (FM) - paramagnetic (PM) phase transitions problem in quantum materials which may be established by Barabási-Albert (BA) scale-free network that possesses power law degree distribution and specific degree correlations. We represent quantum material by means of Dicke-Ising model, that describes the interaction between a spin-1/2 (two-level) system and external classical (magnetic) and quantized (transverse) fields. To describe PM-FM and SR phase transitions we introduce three order parameters: the total (topologically) we

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Crofts, Jonathan J., and Desmond J. Higham. "A weighted communicability measure applied to complex brain networks." Journal of The Royal Society Interface 6, no. 33 (2009): 411–14. http://dx.doi.org/10.1098/rsif.2008.0484.

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Recent advances in experimental neuroscience allow non-invasive studies of the white matter tracts in the human central nervous system, thus making available cutting-edge brain anatomical data describing these global connectivity patterns. Through magnetic resonance imaging, this non-invasive technique is able to infer a snapshot of the cortical network within the living human brain. Here, we report on the initial success of a new weighted network communicability measure in distinguishing local and global differences between diseased patients and controls. This approach builds on recent advanc

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15

Zhang, Peng, and Qi Shuang Ma. "A Method of Evaluating Reliability of More-Electric-Aircraft Power System Using Node-Weighted Network." Advanced Materials Research 516-517 (May 2012): 1288–91. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1288.

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More-electric-aircraft contains more electric equipment compared with conventional aircraft, which makes it have more complex architecture in power system. It has some characters of complex networks, needs new method to analyze. The method of evaluating reliability of more-electric-aircraft power system mentioned in [1] treats all the power load nodes as the same, which does not exactly match the actual system. In this paper, one type of node-weighted network model is proposed. In the node-weighted model, load nodes with different importance are weighted differently. The given example demonstr

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Chen, Shuo, Zhen Zhang, Chen Mo, Qiong Wu, Peter Kochunov, and L. Elliot Hong. "Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis." Entropy 22, no. 9 (2020): 925. http://dx.doi.org/10.3390/e22090925.

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We propose a new metric to characterize the complexity of weighted complex networks. Weighted complex networks represent a highly organized interactive process, for example, co-varying returns between stocks (financial networks) and coordination between brain regions (brain connectivity networks). Although network entropy methods have been developed for binary networks, the measurement of non-randomness and complexity for large weighted networks remains challenging. We develop a new analytical framework to measure the complexity of a weighted network via graph embedding and point pattern analy

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CRIADO, REGINO, MIGUEL ROMANCE, and ÁNGEL SÁNCHEZ. "A POST-PROCESSING METHOD FOR INTEREST POINT LOCATION IN IMAGES BY USING WEIGHTED LINE-GRAPH COMPLEX NETWORKS." International Journal of Bifurcation and Chaos 22, no. 07 (2012): 1250163. http://dx.doi.org/10.1142/s0218127412501635.

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The theory and tools of Complex Networks have not been much applied to Image Analysis and Computer Vision problems. This paper introduces a new method for detecting interest points in digital images making use of Complex Network Analysis. This analysis includes a self-consistent post-processing procedure that improves the localization of the initially detected interest points in the image. We propose a general post-processing localization method based on centrality measures on a weighted version of the line-graph L(G) after the association of a spatial and weighted complex network G to each im

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Huang, Ailing, Jie Xiong, Jinsheng Shen, and Wei Guan. "Evolution of weighted complex bus transit networks with flow." International Journal of Modern Physics C 27, no. 06 (2016): 1650064. http://dx.doi.org/10.1142/s0129183116500649.

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Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled inter

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Jacob, Rinku, K. P. Harikrishnan, R. Misra, and G. Ambika. "Weighted recurrence networks for the analysis of time-series data." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (2019): 20180256. http://dx.doi.org/10.1098/rspa.2018.0256.

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Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) w

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Huang, Xiangyu, Zhongwei Wang, Yan Pang, Wujun Tian, and Ming Zhang. "Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis." Forests 15, no. 9 (2024): 1665. http://dx.doi.org/10.3390/f15091665.

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This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, t

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Beckett, Stephen J. "Improved community detection in weighted bipartite networks." Royal Society Open Science 3, no. 1 (2016): 140536. http://dx.doi.org/10.1098/rsos.140536.

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Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal community partition of a network can be found by searching for the partition that maximiz

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FANG, JINQING, YONG LI, and QIAO BI. "FROM A HARMONIOUS UNIFYING HYBRID PREFERENTIAL MODEL TOWARD A LARGE UNIFYING HYBRID NETWORK MODEL." International Journal of Modern Physics B 21, no. 30 (2007): 5121–42. http://dx.doi.org/10.1142/s0217979207038149.

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The motivation of this work raises four challenging questions: (1) Why is it that so many generalized random network models exist but they cannot be completely consistent with real-world networks? (2) Are these complex networks fundamentally attached in a random preferential manner without any deterministic attachment for both un-weighted and weighted networks? To answer the first two questions, we propose a harmonious unifying hybrid preferential model (HUHPM) controlled by a total hybrid ratio. (3) Why are social networks mostly positive degree-degree correlation but biological and technolog

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Wang, Xin, Feng Chen, Tao Zhang, Ning Qin, and Zhong-Yuan Jiang. "A new routing strategy limited by heterogeneous link bandwidth." Modern Physics Letters B 32, no. 24 (2018): 1850292. http://dx.doi.org/10.1142/s0217984918502925.

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Traffic capacity of a network is very vital to a variety of complex networks, such as communication networks and road networks, in which the bandwidth of every link is limited or finite. In this work, inspired by the deployment process of nodes and links in real networks, we assume the bandwidth of every link is composed of a constant part and a degree-related one that can be updated iteratively with the network hardware update. We propose a link bandwidth-based routing mechanism to enhance the network traffic capacity. Extensive simulations in both scale-free networks and random networks are

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Yin, Yong, Yu Li, Wei Guo Lin, and Er Shi Hua. "Directed-Weighted Modeling of Resource Node in Manufacturing Grid and Its Application Analysis." Advanced Materials Research 472-475 (February 2012): 2626–31. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2626.

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Complex network has penetrated into numerous fields including mathematics and sciences, life science and engineering discipline, with strong interdisciplinary features. This paper applies the complex network theory into Manufacturing Grid field by abstracting multi-node network in Manufacturing Grid into complex network. Firstly, the characteristics of the direction and weight of the resource node in Manufacturing Grid are analyzed. Then, complex network modeling of resource nodes in Manufacturing Grid is launched. Some possible application such as node degree, shortest path between two nodes

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GUO, LONG, and XU CAI. "DEGREE AND WEIGHTED PROPERTIES OF THE DIRECTED CHINA RAILWAY NETWORK." International Journal of Modern Physics C 19, no. 12 (2008): 1909–18. http://dx.doi.org/10.1142/s012918310801331x.

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As well known, many real complex systems are directed and weighted ones. For understanding the topology structure of the directed China Railway Network (CRN) further, we analyze the degree properties of the directed CRN and propose a new method to measure the weight of station (i.e., the utilized efficiency of station) in CRN according to how CRN works really. Rigorous analysis of the existing CRN data shows that the CRN is an assortative network with scale-free degree distribution in space L. On the other hand, the cumulative distribution of station's relative weight, the cumulative distribut

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Stanimirovic, Predrag, Xue-Zhong Wang, and Haifeng Ma. "Complex ZNN for computing time-varying weighted pseudo-inverses." Applicable Analysis and Discrete Mathematics 13, no. 1 (2019): 131–64. http://dx.doi.org/10.2298/aadm170628019s.

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We classify, extend and unify various generalizations of weighted Moore-Penrose inverses in indefinite inner product spaces. New kinds of generalized inverses are introduced for this purpose. These generalized inverses are included in the more general class called as the weighted indefinite pseudoinverses (WIPI), which represents an extension of the Minkowski inverse (MI), the weighted Minkowski inverse (WMI), and the generalized weighted Moore- Penrose (GWM-P) inverse. The WIPI generalized inverses are introduced on the basis of two Hermitian invertible matrices and two Hermitian involuntary

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Wang, Xingyuan, and Xiaomeng Qin. "Detecting communities by asymmetric intimacy in directed-weighted network." International Journal of Modern Physics C 28, no. 01 (2017): 1750006. http://dx.doi.org/10.1142/s0129183117500061.

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Community detection and analysis have attracted wide public concerns over the recent years. Meanwhile, many related algorithms in complex networks have been proposed. However, most of them concentrate on undirected and unweighted networks. Concerning the significant theoretical value and potential application foreground for directed-weighted networks, in this paper, a novel hierarchical communities detection algorithm (termed as DCBAI) has been proposed on the basis of asymmetric intimacy between nodes. Community structures are effectively detected by node clustering algorithm in directed-weig

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Dapena, Adriana, Daniel Iglesia, Francisco J. Vazquez-Araujo, and Paula M. Castro. "New Computation of Resolving Connected Dominating Sets in Weighted Networks." Entropy 21, no. 12 (2019): 1174. http://dx.doi.org/10.3390/e21121174.

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In this paper we focus on the issue related to finding the resolving connected dominating sets (RCDSs) of a graph, denoted by G. The connected dominating set (CDS) is a connected subset of vertices of G selected to guarantee that all vertices in the graph are connected to vertices in the CDS. The connected dominating set with minimum cardinality, or minimum CDS (MCDS), is an adequate virtual backbone for information interchange in a network. When distinct vertices of G have also distinct representations with respect to a subset of vertices in the MCDS, it is said that the MCDS includes a resol

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Li, Zhang-Wei, Xu-Hua Yang, Feng-Ling Jiang, Guang Chen, Guo-Qing Weng, and Mei Zhu. "Dynamically Weighted Clique Evolution Model in Clique Networks." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/182638.

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This paper proposes a weighted clique evolution model based on clique (maximal complete subgraph) growth and edge-weight driven for complex networks. The model simulates the scheme of real-world networks that the evolution of networks is likely to be driven by the flow, such as traffic or information flow needs, as well as considers that real-world networks commonly consist of communities. At each time step of a network’s evolution progress, an edge is randomly selected according to a preferential scheme. Then a new clique which contains the edge is added into the network while the weight of t

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Agarwal, Ankit, Norbert Marwan, Rathinasamy Maheswaran, Ugur Ozturk, Jürgen Kurths, and Bruno Merz. "Optimal design of hydrometric station networks based on complex network analysis." Hydrology and Earth System Sciences 24, no. 5 (2020): 2235–51. http://dx.doi.org/10.5194/hess-24-2235-2020.

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Abstract. Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies

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TIRADO, MARIANO. "COMPLEX NETWORK FOR A CRISIS CONTAGION ON AN INTERBANK SYSTEM." International Journal of Modern Physics C 23, no. 09 (2012): 1250058. http://dx.doi.org/10.1142/s0129183112500581.

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The main focus of this research is the contagion of a financial crisis on an interbank debt network. In order to simulate the crisis propagation a weighted community complex network based on growth strategy has been created. The contagion is described by a new way of disease propagation perspective based on the concept of a financial virus. The model reproduces the existence of TBTF banks and shows the impact that an initial TBTF bank crash produces in the interbank network depending on the magnitude of the initial crash and on the resistance that the network offers against the contagion propa

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Pan, Weifeng, Bo Hu, Bo Jiang, and Bo Xie. "Identifying Important Packages of Object-Oriented Software Using Weighted k-Core Decomposition." Journal of Intelligent Systems 23, no. 4 (2014): 461–76. http://dx.doi.org/10.1515/jisys-2014-0015.

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AbstractIdentifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we introduc

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Zongbo, Yang. "Solving the Node-Weighted Steiner Tree Problem using Reinforcement Learning." International Journal of Engineering Research & Science (IJOER) 10, no. 8 (2024): 49–58. https://doi.org/10.5281/zenodo.13605810.

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The node-weighted Steiner tree problem is a significant issue in network design, with broad applications including telecommunications network construction, offshore oilfield development planning, and wireless ad-hoc networks. The objective of the node-weighted Steiner tree problem is to find a subtree within a given undirected graph that has the minimum total weight and includes all specified terminals. This problem is NP-hard, typically requiring complex algorithm design and exponential time. We have combined graph neural networks and deep reinforcement learning techniques to propose a new so

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He, Lei, Haijun Wei, and Qixuan Wang. "A New Target Detection Method of Ferrography Wear Particle Images Based on ECAM-YOLOv5-BiFPN Network." Sensors 23, no. 14 (2023): 6477. http://dx.doi.org/10.3390/s23146477.

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For mechanical equipment, the wear particle in the lubrication system during equipment operation can reflect the lubrication condition, wear mechanism, and severity of wear between equipment friction pairs. To solve the problems of false detection and missed detection of small, dense, and overlapping wear particles in the current ferrography wear particle detection model in a complex oil background environment, a new ferrography wear particle detection network, EYBNet, is proposed. Firstly, the MSRCR algorithm is used to enhance the contrast of wear particle images and reduce the interference

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Tian, Feng, Dan Wang, Qin Wu, and Daijun Wei. "An empirical study on network conversion of stock time series based on STL method." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 10 (2022): 103111. http://dx.doi.org/10.1063/5.0089059.

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A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time

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Wang, Wanxiang, and Ruijun Guo. "Travel Time Reliability of Highway Network under Multiple Failure Modes." Sustainability 14, no. 12 (2022): 7256. http://dx.doi.org/10.3390/su14127256.

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Network reliability reflects a system’s ability to perform specified functions under specified topological and traffic conditions. Network reliability is the weighted sum of connection reliability and travel time reliability. Based on complex network theory, a new method was proposed to calculate the travel time reliability of road networks. The topology model of a regional highway network in China was built using the dual method. After a random attack or deliberate attack, node sizes in the sub-network can be used to reflect the node importance for network connection reliability. Some conclus

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Deng, Changjian. "The Robustness Analysis of Wireless Sensor Networks under Uncertain Interference." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/185970.

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Based on the complex network theory, robustness analysis of condition monitoring wireless sensor network under uncertain interference is present. In the evolution of the topology of sensor networks, the density weighted algebraic connectivity is taken into account, and the phenomenon of removing and repairing the link and node in the network is discussed. Numerical simulation is conducted to explore algebraic connectivity characteristics and network robustness performance. It is found that nodes density has the effect on algebraic connectivity distribution in the random graph model; high densi

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SIENKIEWICZ, JULIAN, and JANUSZ A. HOŁYST. "SCALING OF INTERNODE DISTANCES IN WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics C 21, no. 06 (2010): 731–39. http://dx.doi.org/10.1142/s0129183110015439.

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We extend the previously observed scaling equation connecting the internode distances and nodes' degrees onto the case of weighted networks. We show that the scaling takes a similar form in the empirical data obtained from networks characterized by different relations between node's strength and its degree. In the case of explicit equation for s (k) (e.g. linear or scale-free), the new coefficients of scaling equation can be easily obtained. We support our analysis with numerical simulations for Erdös–Rényi random graphs with different weight distributions.

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Képes, Tamás-Zsolt. "The critical node detection problem in hypergraphs using weighted node degree centrality." PeerJ Computer Science 9 (May 3, 2023): e1351. http://dx.doi.org/10.7717/peerj-cs.1351.

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Network analysis is an indispensable part of today’s academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical no

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Zhou, Yunpeng, Jinyu Wu, Rentao Liu, and Zixuan Yu. "Spatial Economic Correlations via Geographically Weighted Neural Network Regression with A New Dataset." Applied and Computational Engineering 132, no. 1 (2025): 55–69. https://doi.org/10.54254/2755-2721/2024.20531.

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The GDP and unemployment rates of two geographic units are influenced by their population density and economic characteristics, as well as their distance and spatial relationship. This work aim to use Geographically Weighted Regression (GWR), a classic and widely used method for modeling spatial heterogeneity, to analyze the correlation of these multiple factors. However, GWR does not precisely express its weighting kernel, making it insufficient to estimate complex geographic processes. Therefore, the work employed the Geographically Weighted Neural Network Regression (GNNWR) model, which com

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Yang, Huijuan, and Meilong Le. "High-Order Community Detection in the Air Transport Industry: A Comparative Analysis among 10 Major International Airlines." Applied Sciences 11, no. 20 (2021): 9378. http://dx.doi.org/10.3390/app11209378.

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Community detection in a complex network is an ongoing field. While the air transport network has gradually formed as a complex system, the topological and geographical characteristics of airline networks have become crucial in understanding the network dynamics and airports’ roles. This research tackles the highly interconnected parts in weighted codeshare networks. A dataset comprising ten major international airlines is selected to conduct a comparative analysis. The result confirms that the clique percolation method can be used in conjunction with other metrics to shed light on air transpo

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Zhang, Di, Limin Jia, Jin Ning, Yujiang Ye, Hao Sun, and Ruifeng Shi. "Power Grid Structure Performance Evaluation Based on Complex Network Cascade Failure Analysis." Energies 16, no. 2 (2023): 990. http://dx.doi.org/10.3390/en16020990.

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A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance a

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Zhao, Fang, Hongmei Cao, and Tianwei Lu. "What Impact Will the New-Built Metro Bring to the Transportation of Second-Tier Cities? From the Perspective of a Multilayer Complex Network." Urban Rail Transit 7, no. 2 (2021): 117–27. http://dx.doi.org/10.1007/s40864-021-00146-7.

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AbstractMetro is being developed rapidly in second-tier cities. There is a need to understand the impact it brings as it relates to the planning and management of the whole urban transportation system. In this paper, we applied the multilayer complex network theory to study this problem by contrasting the characteristics of transportation networks before and after the metro is built. We focused on transportation networks in second-tier cities and (1) proposed edge functions of the road subnetwork and rail transit subnetwork with impedance as weight; (2) established an interlayer function based

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Low, Kian Hsiang, Wee Kheng Leow, and Marcelo H. Ang. "An Ensemble of Cooperative Extended Kohonen Maps for Complex Robot Motion Tasks." Neural Computation 17, no. 6 (2005): 1411–45. http://dx.doi.org/10.1162/0899766053630378.

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Self-organizing feature maps such as extended Kohonen maps (EKMs) have been very successful at learning sensorimotor control for mobile robot tasks. This letter presents a new ensemble approach, cooperative EKMs with indirect mapping, to achieve complex robot motion. An indirect-mapping EKM self-organizes to map from the sensory input space to the motor control space indirectly via a control parameter space. Quantitative evaluation reveals that indirect mapping can provide finer, smoother, and more efficient motion control than does direct mapping by operating in a continuous, rather than disc

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Yang, Bin, Xin Wang, Jian-an Fang, and Yuhua Xu. "The Impact of Coupling Function on Finite-Time Synchronization Dynamics of Multi-Weighted Complex Networks with Switching Topology." Complexity 2019 (March 21, 2019): 1–15. http://dx.doi.org/10.1155/2019/7276152.

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This paper is not only concerned with the problem of finite-time synchronization control for a class of nonlinear coupling multi-weighted complex networks (NCMWCNs) with switching topology but also an attempt at using the derived results and Lyapunov stability theory to study the impact of nonlinear coupling function on finite-time synchronization dynamics of the raised network model. Firstly, different from the existing related results, based on the existing and new finite-time theories, two finite-time synchronization controllers are, respectively, designed to make the considered network ach

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Zhao, Xue. "Aviation application of dual-weighted neural network based on biomimetic pattern recognition." Journal of Physics: Conference Series 2252, no. 1 (2022): 012059. http://dx.doi.org/10.1088/1742-6596/2252/1/012059.

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Abstract In order to solve the difficult problem of fault diagnosis in aviation equipment, this paper uses a biomimetic pattern recognition method. Compared to traditional pattern recognition, biomimetic pattern recognition is able to construct closed, complex geometries to cover all kinds of samples. Biomimetic pattern recognition is a “cognitive” approach, where the process between two similar things is considered asymptotic. It mathematically means that all similar samples in the feature space are continuously indistinguishable. The paper establishes a dual-weighted neural network model of

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Zhao, Xue. "Aviation application of dual-weighted neural network based on biomimetic pattern recognition." Journal of Physics: Conference Series 2252, no. 1 (2022): 012059. http://dx.doi.org/10.1088/1742-6596/2252/1/012059.

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Abstract In order to solve the difficult problem of fault diagnosis in aviation equipment, this paper uses a biomimetic pattern recognition method. Compared to traditional pattern recognition, biomimetic pattern recognition is able to construct closed, complex geometries to cover all kinds of samples. Biomimetic pattern recognition is a “cognitive” approach, where the process between two similar things is considered asymptotic. It mathematically means that all similar samples in the feature space are continuously indistinguishable. The paper establishes a dual-weighted neural network model of

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Midoun, Mohamed Amine, and Xingyuan Wang. "New magnetic algorithm to detect community structure based on the magnets’ approach." Modern Physics Letters B 33, no. 13 (2019): 1950166. http://dx.doi.org/10.1142/s0217984919501665.

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This research paper investigates areas of identifying the community structures which can help us to uncover organizational principles in complex networks. So far, a large number of algorithms have been introduced to discover the community structures, but the majority of these algorithms are suitable just for the unweighted networks. We present a new hierarchical method that identifies communities in weighted and unweighted networks based on the magnetic field. In our approach, the nodes are considered as a set of stationary/mobile magnetic nodes where these magnets can attract and repulse unde

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Alvarez, Alejandro J., Carlos E. Sanz-Rodríguez, and Juan Luis Cabrera. "Weighting dissimilarities to detect communities in networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2056 (2015): 20150108. http://dx.doi.org/10.1098/rsta.2015.0108.

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Many complex systems can be described as networks exhibiting inner organization as communities of nodes. The identification of communities is a key factor to understand community-based functionality. We propose a family of measures based on the weighted sum of two dissimilarity quantifiers that facilitates efficient classification of communities by tuning the quantifiers’ relative weight to the network’s particularities. Additionally, two new dissimilarities are introduced and incorporated in our analysis. The effectiveness of our approach is tested by examining the Zachary’s Karate Club Netwo

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Lai, Yifan, and Haoran Gao. "PCI Planning and Solution Based on Point Weighted Simulated Annealing Algorithm." Highlights in Business, Economics and Management 53 (March 17, 2025): 170–78. https://doi.org/10.54097/nv1gfk68.

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Physical cell identification (PCI) collision, confusion and interference are key problems in modern LTE network optimization, which significantly affect network performance and user experience. Traditional optimization methods that rely on manual adjustment and empirical rules are inefficient and difficult to adapt to complex network environments. This paper presents an optimization method based on simulated annealing to minimize PCI conflict, confusion and interference. Through the collection and pre-processing of user device MR Data and network topology data, combined with feature engineerin

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