Статті в журналах: "Weighted Complex Network"

1

Lan, Wang Sen, and Guo Hao Zhao. "Detecting Backbone of Weighted Complex Network." Advanced Materials Research 143-144 (October 2010): 712–16. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.712.

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In order to explore key nodes natures and find out the core of weighted networks, the study advanced backbone network (BN) conception, developed largest eigenvalue algorithm of weight matrix (LEAWM) which utilized matrix characteristic spectrum to detect BN nodes, and done empirical research for two networks: (1) US air lines network, (2) stocks network of coal and power sectors in china stock market. The empirical results indicate that LEAWM is efficient for detecting the BN nodes with some important properties such as bigger degree and betweenness, BN is the core and backbone of its mother network.

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2

Dai, Meifeng, Yongbo Hou, Tingting Ju, Changxi Dai, Yu Sun, and Weiyi Su. "Weighted trapping time of weighted directed treelike network." International Journal of Modern Physics C 31, no. 08 (July 10, 2020): 2050108. http://dx.doi.org/10.1142/s0129183120501089.

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With the deepening of research on complex networks, many properties of complex networks are gradually studied, for example, the mean first-passage times, the average receive times and the trapping times. In this paper, we further study the average trapping time of the weighted directed treelike network constructed by an iterative way. Firstly, we introduce our model inspired by trade network, each edge [Formula: see text] in undirected network is replaced by two directed edges with weights [Formula: see text] and [Formula: see text]. Then, the trap located at central node, we calculate the weighted directed trapping time (WDTT) and the average weighted directed trapping time (AWDTT). Remarkably, the WDTT has different formulas for even generations and odd generations. Finally, we analyze different cases for weight factors of weighted directed treelike network.

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3

Mohmand, Yasir Tariq, and Aihu Wang. "Weighted Complex Network Analysis of Pakistan Highways." Discrete Dynamics in Nature and Society 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/862612.

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The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.

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4

Xu, Shuang, Chunxia Zhang, Pei Wang, and Jiangshe Zhang. "Variational Bayesian weighted complex network reconstruction." Information Sciences 521 (June 2020): 291–306. http://dx.doi.org/10.1016/j.ins.2020.02.050.

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5

XU, XIN-JIAN, ZHI-XI WU, and YING-HAI WANG. "PROPERTIES OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics C 17, no. 04 (April 2006): 521–29. http://dx.doi.org/10.1142/s0129183106008662.

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We study two kinds of weighted networks, weighted small-world (WSW) and weighted scale-free (WSF). The weight wij of a link between nodes i and j in the network is defined as the product of endpoint node degrees; that is wij =(ki kj)θ. In contrast to adding weights to links when networks are being constructed, we only consider weights depending on the "popularity" of the nodes represented by their connectivity. It was found that both weighted networks have broad distributions on the characterization of the link weight, the vertex strength, and the average shortest path length. Furthermore, as a survey of the model, the epidemic spreading process in both weighted networks was studied based on the standard susceptible-infected (SI) model. The spreading velocity reaches a peak very quickly after the infection outbreaks and an exponential decay was found in the long time propagation.

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6

LEUNG, C. C., and H. F. CHAU. "WEIGHTED ACCELERATED GROWTH MODEL OF COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 23n24 (September 30, 2007): 4064–66. http://dx.doi.org/10.1142/s0217979207045219.

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We introduce and study a toy model which mimics the structure formation of a typical weighted network in the real world. In particular, the organizational structures of our networks are found to be consistent with real-world networks.

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7

WANG, XUTAO, HONGTAO LU, and GUANRONG CHEN. "THE MODELLING OF WEIGHTED COMPLEX NETWORKS." International Journal of Modern Physics B 21, no. 16 (June 20, 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 networks more precisely.

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8

Long, Hao, and Xiao-Wei Liu. "Multiresolution community detection in weighted complex networks." International Journal of Modern Physics C 30, no. 02n03 (February 2019): 1950016. http://dx.doi.org/10.1142/s0129183119500165.

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The community is the dominant structure that exhibits different features and multifold functions of complex networks from different levels; accordingly, multiresolution community detection is of critical importance in network science. In this paper, inspired by the ideas of the network flow, we propose an intensity-based community detection algorithm, i.e. ICDA, to detect multiresolution communities in weighted networks. First, the edge intensity is defined to quantify the relationship between each pair of connected nodes, and the vertices connected by the edges with higher intensities are denoted as core nodes, while the others are denoted as marginal nodes. Second, by applying the expansion strategy, the algorithm merges the closely connected core nodes as the initial communities and attaches marginal nodes to the nearest initial communities. To guarantee a higher internal density for the ultimate communities, the captured communities are further adjusted according to their densities. Experimental results of real and synthetic networks illustrate that our approach has higher performance and better accuracy. Meanwhile, a multiresolution investigation of some real networks shows that the algorithm can provide hierarchical details of complex networks with different thresholds.

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9

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 reliability and robustness of the RTS network. Simulation results show that the distribution of nodes strength exhibits a power-law behavior and Shanghai RTS network shows a strong weighted rich-club effect. This study also indicates that the intentional attacks are more detrimental to the RTS network than to the random weighted network, but the random attacks can cause slightly more damage to the random weighted network than to the RTS network. Our results provide a richer view of complex weighted networks in real world and possibilities of risk analysis and policy decisions for the RTS operation department.

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10

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 measure is WEFF, the CondWBet strategy is the best to decrease WEFF in 3 out of 5 cases. Further, CondWBet is the most effective strategy to reduce WEFF at the beginning of the removal process, whereas the Strength that removes nodes with the highest sum of the link weights first shows the highest efficacy in the final phase of the removal process when the network is broken into many small clusters. These last outcomes would suggest that a better attacking in weighted networks strategy could be a combination of the CondWBet and Strength strategies.

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11

Gao, Zhan, Qing Bo Zhu, and Chun Mei Wei. "Complex Wireless Sensor Network Routing Strategy for Information Rapid Transmission." Advanced Materials Research 1037 (October 2014): 201–4. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.201.

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For wireless sensor networks for energy requirements are very high and limited node energy characteristics of wireless sensor networks to improve information transfer for the purpose of quick study proposes a wireless sensor network nodes spread weighted routing strategy. The simulation result were weighted node degree technical analysis, analysis of the advantages from the principle of routing policy change, thereby effectively increasing the network lifetime and improve the data transfer rate and reduce the transmission delay, is more suitable for large-scale wireless sensor network.

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12

WANG, H., E. VAN BOVEN, A. KRISHNAKUMAR, M. HOSSEINI, H. VAN HOOFF, T. TAKEMA, N. BAKEN, and P. VAN MIEGHEM. "MULTI-WEIGHTED MONETARY TRANSACTION NETWORK." Advances in Complex Systems 14, no. 05 (October 2011): 691–710. http://dx.doi.org/10.1142/s021952591100330x.

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This paper aims to both develop and apply advances from the field of complex networks to large economic systems and explore the (dis)similarities between economic systems and other real-world complex networks. For the first time, the nature and evolution of the Dutch economy are captured by means of a data set analysis that describes the monetary transactions among 105 economical activity clusters over the period 1987–2007. We propose to represent this data set as a multi-weighted network, called the monetary transaction network. Each node represents a unique activity cluster. Nodes are interconnected via monetary transactions. The millions of euros that traverse the links and that circulate inside each activity cluster are denoted by a link weight and a node weight respectively. By applying innovative methodologies from network theory, we observe important features of the monetary transaction network as well as its evolution: (a) Activity clusters with a large internal flow tend to cooperate with many other clusters via high volume monetary transactions. (b) Activity clusters with a lower internal transaction volume prefer to transact with fewer neighboring nodes that have a higher internal flow. (c) The node weights seem to follow a power law distribution. Surprisingly, (b) and (c) have been observed in community structures of many real-world networks as well. (d) Activity clusters tend to balance the monetary volume of their transactions with their neighbors, reflected by a positive link weight correlation around each node. This correlation becomes stronger over time while the number of links increases over time as well.

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13

TASGIN, MURSEL, and HALUK O. BINGOL. "GOSSIP ON WEIGHTED NETWORKS." Advances in Complex Systems 15, supp01 (June 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 spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.

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14

Wang, Fu Yan, Sha Qiu, and Qing Li. "Complex Opinion Network Correlation Clustering." Applied Mechanics and Materials 644-650 (September 2014): 2846–49. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2846.

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In this paper, 2112 specific correlation data of 2 types cluster were selected as sample to build a weighted network, including each hour sample is represented by a vertex and a correlation between 2 clusters is represented by an edge. We analysis this network structure by complex network theory and computer method. We found that the correlation clusters of 2 media have an important impact on this complex network, and the specific sample follow a frequency distribution of the weighted degrees. Applying the method of k-core shows small groups in this complex network, also the modularity calculating help us find out the key cluster, the correlation cluster, the medium cluster and the interaction path of them. An apparently small-world effect has found by the shortest path calculating effectively. All of these may provide a scientific and reasonable reference for further research.

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15

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 (May 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 the underlying topological attributes of these networks. Our results supported that the T1w/T2w ratio brain networks were comprised of small-worldness, an exponentially truncated power–law degree distribution, frontal-parietal hubs and modular organization. Besides, there were significant positive correlations between the network metrics and fluid intelligence. Thus, the T1w/T2w ratio brain networks open a new avenue to understand the human brain and are a necessary supplement for future MRI studies.

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16

Chen, Wei, Manrui Jiang, Cheng Jiang, and Jun Zhang. "Critical node detection problem for complex network in undirected weighted networks." Physica A: Statistical Mechanics and its Applications 538 (January 2020): 122862. http://dx.doi.org/10.1016/j.physa.2019.122862.

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17

Qian, Liqiang, Zhan Bu, Mei Lu, Jie Cao, and Zhiang Wu. "Extracting Backbones from Weighted Complex Networks with Incomplete Information." Abstract and Applied Analysis 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/105385.

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The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.

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18

马, 领娟. "Combination Model of Weighted Complex Network Community Structure." Advances in Applied Mathematics 10, no. 05 (2021): 1592–97. http://dx.doi.org/10.12677/aam.2021.105168.

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19

He, Xuan, Luyang Wang, Hongbo Zhu, and Zheng Liu. "Statistical analysis of complex weighted network for seismicity." Physica A: Statistical Mechanics and its Applications 563 (February 2021): 125468. http://dx.doi.org/10.1016/j.physa.2020.125468.

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20

Wu, Shaojie, Yan Zhu, Ning Li, Yizeng Wang, Xingju Wang, and Daniel Jian Sun. "Urban Rail Transit System Network Reliability Analysis Based on a Coupled Map Lattice Model." Journal of Advanced Transportation 2021 (April 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/5548956.

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During the last twenty years, the complex network modeling approach has been introduced to assess the reliability of rail transit networks, in which the dynamic performance involving passenger flows have attracted more attentions during operation stages recently. This paper proposes the passenger-flow-weighted network reliability evaluation indexes, to assess the impact of passenger flows on network reliability. The reliability performances of the rail transit network and passenger-flow-weighted one are analyzed from the perspective of a complex network. The actual passenger flow weight of urban transit network nodes was obtained from the Shanghai Metro public transportation card data, which were used to assess the reliability of the passenger-flow-weighted network. Furthermore, the dynamic model of the Shanghai urban rail transit network was constructed based on the coupled map lattice (CML) model. Then, the processes of cascading failure caused by network nodes under different destructive situations were simulated, to measure the changes of passenger-flow-weighted network reliability during the processes. The results indicate that when the scale of network damage attains 50%, the reliability of the passenger-flow-weighted network approaches zero. Consequently, taking countermeasures during the initial stage of network cascading may effectively prevent the disturbances from spreading in the network. The results of the paper could provide guidelines for operation management, as well as identify the unreliable stations within passenger-flow-weighted networks.

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21

SIMAS, TIAGO, and LUIS M. ROCHA. "Distance closures on complex networks." Network Science 3, no. 2 (March 30, 2015): 227–68. http://dx.doi.org/10.1017/nws.2015.11.

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AbstractTo expand the toolbox available to network science, we study the isomorphism between distance and Fuzzy (proximity or strength) graphs. Distinct transitive closures in Fuzzy graphs lead to closures of their isomorphic distance graphs with widely different structural properties. For instance, the All Pairs Shortest Paths (APSP) problem, based on the Dijkstra algorithm, is equivalent to a metric closure, which is only one of the possible ways to calculate shortest paths in weighted graphs. We show that different closures lead to different distortions of the original topology of weighted graphs. Therefore, complex network analyses that depend on the calculation of shortest paths on weighted graphs should take into account the closure choice and associated topological distortion. We characterize the isomorphism using the max-min and Dombi disjunction/conjunction pairs. This allows us to: (1) study alternative distance closures, such as those based on diffusion, metric, and ultra-metric distances; (2) identify the operators closest to the metric closure of distance graphs (the APSP), but which are logically consistent; and (3) propose a simple method to compute alternative path length measures and corresponding distance closures using existing algorithms for the APSP. In particular, we show that a specific diffusion distance is promising for community detection in complex networks, and is based on desirable axioms for logical inference or approximate reasoning on networks; it also provides a simple algebraic means to compute diffusion processes on networks. Based on these results, we argue that choosing different distance closures can lead to different conclusions about indirect associations on network data, as well as the structure of complex networks, and are thus important to consider.

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22

Yang, Yunyun, Gang Xie, and Jun Xie. "Mining Important Nodes in Directed Weighted Complex Networks." Discrete Dynamics in Nature and Society 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/9741824.

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In complex networks, mining important nodes has been a matter of concern by scholars. In recent years, scholars have focused on mining important nodes in undirected unweighted complex networks. But most of the methods are not applicable to directed weighted complex networks. Therefore, this paper proposes a Two-Way-PageRank method based on PageRank for further discussion of mining important nodes in directed weighted complex networks. We have mainly considered the frequency of contact between nodes and the length of time of contact between nodes. We have considered the source of the nodes (in-degree) and the whereabouts of the nodes (out-degree) simultaneously. We have given node important performance indicators. Through numerical examples, we analyze the impact of variation of some parameters on node important performance indicators. Finally, the paper has verified the accuracy and validity of the method through empirical network data.

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23

Liu, Weiyan, Xin Li, Tao Liu, and Bin Liu. "Approximating Betweenness Centrality to Identify Key Nodes in a Weighted Urban Complex Transportation Network." Journal of Advanced Transportation 2019 (February 14, 2019): 1–8. http://dx.doi.org/10.1155/2019/9024745.

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The key nodes in a complex transportation network have a significant influence on the safety of traffic operations, connectivity reliability, and the performance of the entire network. However, the identification of key nodes in existing urban transportation networks has mainly focused on nonweighted networks and the network information of the nodes themselves, which do not accurately reflect their global status. Thus, the present study proposes a key node identification algorithm that combines traffic flow features and is based on weighted betweenness centrality. This study also uses weighted roads to construct an L-space weighted transportation network and an approximate algorithm for betweenness centrality in order to reduce the complexity of the calculations. The results of the simulation indicate that the proposed algorithm is not only capable of identifying the key nodes in a relatively short amount of time, but it does so with high accuracy. The findings of this study can be used to provide decision-making support for road network management, planning, and urban traffic construction optimization.

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24

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 (February 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 add a new node and m links, but also to remove an old node and corresponding links with probability at each time step. By using rate equation and mean-field method, we study the network's properties: The weight, strength and their distributions. We find that the relationship between weight and strength is nonlinear. In addition, we theoretically prove that the weight distribution and the strength distribution follow a power-law distribution, respectively.

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25

Chen, Guangfu, Chen Xu, Jingyi Wang, Jianwen Feng, and Jiqiang Feng. "Graph regularization weighted nonnegative matrix factorization for link prediction in weighted complex network." Neurocomputing 369 (December 2019): 50–60. http://dx.doi.org/10.1016/j.neucom.2019.08.068.

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26

Zhan, Zhi Jian, Feng Lin, and Xiao Pin Yang. "Keyword Extraction of Document Based on Weighted Complex Network." Advanced Materials Research 403-408 (November 2011): 2146–51. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2146.

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This document explains and demonstrates how to extract keyword from Chinese document based on weighted complex network. The characteristic and disadvantages of several common automatic keyword extraction methods are introduced firstly. Then based on the ideas of complex network, we proposed an improved automatic keyword extraction method. Using complex network, a Chinese document is first represented as a network: the node represents the term, and the edge represents the Co-Occurrence of terms. Then we calculate the integrate value of each term, the keywords are top k terms with greatest value. The experiment results show that the method is more effective and accurate in comparison with the traditional method TFIDF keyword extraction from the same document.

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27

Zhu, Xuzhen, Qiwen Yang, Hui Tian, Jinming Ma, and Wei Wang. "Contagion of Information on Two-Layered Weighted Complex Network." IEEE Access 7 (2019): 155064–74. http://dx.doi.org/10.1109/access.2019.2948941.

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28

Cao, Jin, Yuan Ge, Dongdong Wang, Qiyou Lin, and Renfeng Chen. "Electric vehicle charging guidance based on weighted complex network." Systems Science & Control Engineering 10, no. 1 (October 27, 2022): 877–86. http://dx.doi.org/10.1080/21642583.2022.2135632.

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29

Zhang, Hui. "Structural Analysis of Bus Networks Using Indicators of Graph Theory and Complex Network Theory." Open Civil Engineering Journal 11, no. 1 (January 30, 2017): 92–100. http://dx.doi.org/10.2174/1874149501711010092.

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The structure of bus network is very significant for bus system. To evaluate the performance of the structure of bus network, indicators basing on graph theory and complex network theory are proposed. Three forms of matrices comprising line-station matrix, weighted adjacency matrix and adjacency matrix under space P are used to represent the bus network. The paper proposes a shift power law distribution which is related average degree of network to fit the degree distribution and a method to calculate the average transfer time between any two stations using adjacency matrix under P space. Moreover, this paper proposes weighted average shortest path distance and transfer efficiency to evaluate the bus network. The results show that the indicators that we introduce, effectively reflect properties of bus network.

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30

Alodjants, A. P., A. Yu. Bazhenov, and M. M. Nikitina. "Phase Transitions in Quantum Complex Networks." Journal of Physics: Conference Series 2249, no. 1 (April 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) weighted as well as un-weighted z-spin components, and the normalized transverse field amplitude, which correspond to the spontaneous magnetization in z- and x-directions, respectively. We have shown that SR state occurs as a result of the interaction between the ordering of the spins in the z− and x-directions and depends on assortativity or disassortativity of the network medium. We have shown that non-trivial topological behavior associated with large fluctuations of network parameters inherent to assortative networks reduces of PM-FM phase transition temperature, while dissasortative networks exhibit high temperature phase transitions. Our findings demonstrate new opportunities to design of quantum materials which may be implemented for current quantum technologies at relatively high temperatures.

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31

Bagler, Ganesh. "Analysis of the airport network of India as a complex weighted network." Physica A: Statistical Mechanics and its Applications 387, no. 12 (May 2008): 2972–80. http://dx.doi.org/10.1016/j.physa.2008.01.077.

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32

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 (September 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 problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.

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33

Bellingeri, Michele, Zhe-Ming Lu, Davide Cassi, and Francesco Scotognella. "Analyses of the response of a complex weighted network to nodes removal strategies considering links weight: The case of the Beijing urban road system." Modern Physics Letters B 32, no. 05 (February 20, 2018): 1850067. http://dx.doi.org/10.1142/s0217984918500677.

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Complex network response to node loss is a central question in different fields of science ranging from physics, sociology, biology to ecology. Previous studies considered binary networks where the weight of the links is not accounted for. However, in real-world networks the weights of connections can be widely different. Here, we analyzed the response of real-world road traffic complex network of Beijing, the most prosperous city in China. We produced nodes removal attack simulations using classic binary node features and we introduced weighted ranks for node importance. We measured the network functioning during nodes removal with three different parameters: the size of the largest connected cluster (LCC), the binary network efficiency (Bin EFF) and the weighted network efficiency (Weg EFF). We find that removing nodes according to weighted rank, i.e. considering the weight of the links as a number of taxi flows along the roads, produced in general the highest damage in the system. Our results show that: (i) in order to model Beijing road complex networks response to nodes (intersections) failure, it is necessary to consider the weight of the links; (ii) to discover the best attack strategy, it is important to use nodes rank accounting links weight.

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34

Gao, Zhong-Ke, Shan Li, Wei-Dong Dang, Yu-Xuan Yang, Younghae Do, and Celso Grebogi. "Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series." International Journal of Bifurcation and Chaos 27, no. 08 (July 2017): 1750123. http://dx.doi.org/10.1142/s0218127417501231.

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Characterizing complicated behavior from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. We in this paper propose a novel wavelet multiresolution complex network (WMCN) for analyzing multivariate nonlinear time series. In particular, we first employ wavelet multiresolution decomposition to obtain the wavelet coefficients series at different resolutions for each time series. We then infer the complex network by regarding each time series as a node and determining the connections in terms of the distance among the feature vectors extracted from wavelet coefficients series. We apply our method to analyze the multivariate nonlinear time series from our oil–water two-phase flow experiment. We construct various wavelet multiresolution complex networks and use the weighted average clustering coefficient and the weighted average shortest path length to characterize the nonlinear dynamical behavior underlying the derived networks. In addition, we calculate the permutation entropy to support the findings from our network analysis. Our results suggest that our method allows characterizing the nonlinear flow behavior underlying the transitions of oil–water flows.

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35

Ma, Zhi-Yi, Xiao-Dong Yang, Ai-Jun He, Lu Ma, and Jun Wang. "Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph." Acta Physica Sinica 71, no. 5 (2022): 050501. http://dx.doi.org/10.7498/aps.71.20211656.

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The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure. Now, visibility graph analysis of univariate time series has become mature gradually. However, most of complex systems in real world are multi-dimensional, so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series. In this paper, a novel method of analyzing the multivariate time series is proposed. For patients with myocardial infarction and healthy subjects, the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series, which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network. Each node of the network corresponds to a lead, and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges. Owing to the fully connected network of different groups showing an identical topological structure, the dynamic characteristics of different individuals cannot be uniquely represented. Therefore, we reconstruct the fully connected network according to inter-layer mutual information, and when the value of inter-layer mutual information is less than the threshold we set, the edge corresponding to the inter-layer mutual information is deleted. We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients. Moreover, multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result. Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects, their reconstructed networks show a more regular structure, higher complexity and connectivity, and the healthy subjects can be distinguished from patients with myocardial infarction, whose reconstructed networks are sparser. Experimental results show that the identification accuracy of both parameters, average weighted degree and average weighted clustering coefficient, reaches 93.3%, which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction, and realize the automatic detection of myocardial infarction.

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36

Liu, Qun. "Spectral analysis for weighted iterated pentagonal graphs and its applications." Modern Physics Letters B 34, no. 28 (June 10, 2020): 2050308. http://dx.doi.org/10.1142/s021798492050308x.

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Deterministic weighted networks have been widely used to model real-world complex systems. In this paper, we study the weighted iterated pentagonal networks. From the construction of the network, we derive recursive relations of all eigenvalues and their multiplicities of its normalized Laplacian matrix from the two successive generations of the weighted iterated pentagonal networks. As applications of spectra of the normalized Laplacian matrix, we study the Kemeny’s constant, the multiplicative degree-Kirchhoff index, and the number of weighted spanning trees and derive their exact closed-form expressions for the weighted iterated pentagonal networks.

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37

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 (May 13, 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 interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

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38

Jiang, Wanchang, and Yinghui Wang. "Node Similarity Measure in Directed Weighted Complex Network Based on Node Nearest Neighbor Local Network Relative Weighted Entropy." IEEE Access 8 (2020): 32432–41. http://dx.doi.org/10.1109/access.2020.2971968.

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39

Li, Ji, Rong Mo, and Ling Ling Liu. "Analysis of Service-Oriented Manufacturing Network Based on Complex Network." Applied Mechanics and Materials 271-272 (December 2012): 401–5. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.401.

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Aiming at the description and analysis of service-oriented manufacturing network, the paper gives its definition on the basis of analyzing the relationship between enterprises and service. Besides, it not only defines, calculates and analyzes the statistic characteristics such as node strength and clustering coefficient by the complex weighted theory, but reveals the status of enterprise and correlations in the manufacturing environment of oriented service. This study can not only provide basis for the construction and management of service-oriented manufacturing network, but for search and grouping of collaborative partners.

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40

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 (September 17, 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 applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

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41

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 (January 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) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.

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42

Xiang, Ying, Rong Mo, Zhiyong Chang, Hu Qiao, and Chunlei Li. "Stability Analysis of Process Route Based on Weighted Network." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/946281.

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Aiming at the production stability of complex parts, a method based on weighted network was proposed to analyze the stability of the process routes of complex parts in order to improve the production stability. The weighted network of the process routes of complex parts was constructed by using the concept of machining cell which can transform the production cost and manufacturing time to the weights of network to do the decision-making of the process routes. Based on the production stability, the brittleness risk entropy of subsystem of weighted network was constructed by analyzing the probability of the brittle events that may lead to the collapse of the weighted network in machining cells. As the indicator of analyzing the vulnerability of weighted network node, the brittleness risk entropy can predict the easily failed subsystem in the entire network. Meanwhile, the brittle event, which may lead to the machining cell failure, was retrospect for the greater stability of the process routes. Finally, the correctness and effectiveness of this method were verified by using the manufacturing process of an aero-engine blade.

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43

Kong, Tianjiao, Jie Shao, Jiuyuan Hu, Xin Yang, Shiyiling Yang, and Reza Malekian. "EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph." Sensors 21, no. 5 (March 7, 2021): 1870. http://dx.doi.org/10.3390/s21051870.

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Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

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44

Huang, Ailing, H. Michael Zhang, Wei Guan, Yang Yang, and Gaoqin Zong. "Cascading Failures in Weighted Complex Networks of Transit Systems Based on Coupled Map Lattices." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/940795.

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Study on the vulnerability and robustness of urban public transit networks (PTNs) has great implications for PTNs planning and emergency management, particularly considering passengers’ dynamic behaviors. We made a complex weighted network analysis based on passenger flow for Beijing’s bus stop network and multimodal transit network coupled with bus and urban rail systems. The analysis shows that there are small-world or scale-free properties in these two networks, which make them display different robustness under link or node failures. With consideration of the dynamic flow redistribution, we propose a model based on coupled map lattices to analyze the cascading failures of these two weighted networks. We find that the dynamic flow redistribution can significantly improve the tolerance of small-world or scale-free PTN against random faults. Because of the coupling of bus and rail systems, the multimodal network with scale-free topology and flow distribution structures displays an increasing tolerance even against intentional attack; however, its cascade is also much more intense once the failure is triggered. We find some thresholds of topological and flow coupling strength in the spreading process, which can be exploited to develop strategies to control cascade failures.

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45

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 demonstrates that this approach is feasibly and rational.

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46

Beckett, Stephen J. "Improved community detection in weighted bipartite networks." Royal Society Open Science 3, no. 1 (January 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 maximizes modularity. Attempting to find the partition that maximizes modularity is a computationally hard problem requiring the use of algorithms. QuanBiMo is an algorithm that has been proposed to maximize weighted modularity in bipartite networks. This paper introduces two new algorithms, LPAwb+ and DIRTLPAwb+, for maximizing weighted modularity in bipartite networks. LPAwb+ and DIRTLPAwb+ robustly identify partitions with high modularity scores. DIRTLPAwb+ consistently matched or outperformed QuanBiMo, while the speed of LPAwb+ makes it an attractive choice for detecting the modularity of larger networks. Searching for modules using weighted data (rather than binary data) provides a different and potentially insightful method for evaluating network partitions.

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47

Mattie, Heather, and Jukka-Pekka Onnela. "Edge overlap in weighted and directed social networks." Network Science 9, no. 2 (February 16, 2021): 179–93. http://dx.doi.org/10.1017/nws.2020.49.

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AbstractWith the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here, we extend definitions of edge overlap to weighted and directed networks and present closed-form expressions for the mean and variance of each version for the Erdős–Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks, and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

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48

Ling, Lü, Chai Yuan, and Luan Ling. "Projective synchronization of spatiotemporal chaos in a weighted complex network." Chinese Physics B 19, no. 8 (August 2010): 080506. http://dx.doi.org/10.1088/1674-1056/19/8/080506.

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49

Lü, Ling, and Chengren Li. "Generalized synchronization of spatiotemporal chaos in a weighted complex network." Nonlinear Dynamics 63, no. 4 (September 18, 2010): 699–710. http://dx.doi.org/10.1007/s11071-010-9831-2.

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50

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 (July 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 image with a prescribed geometrical structure. The practical testing of this fast-computable post-processing method shows that it is self-consistent since the distribution of the centrality measures in the weighted line-graph give us the intrinsic thresholds of interest of each region in the digital image.

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