Journal articles: 'Complex network'

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Hu, Ziping, Krishnaiyan Thulasiraman, and Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure." American Journal of Operations Research 03, no. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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Tan, Yangxin, Junlin Wu, and Qing Zhong. "Complex network." Journal of Physics: Conference Series 1601 (July 2020): 032011. http://dx.doi.org/10.1088/1742-6596/1601/3/032011.

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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.

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Xu, Shuai, and Bai Da Zhang. "Complex Network Model and its Application." Advanced Materials Research 791-793 (September 2013): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.

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Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, however, due to the complexity of these networks, conventional simplified model and cannot be good solution to the above problems. The complex network and network complexity has become a hot issue in the scientific and engineering concern. This article describes a few common complex network models and its application brief.

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Asbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (March 15, 2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.

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Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.

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Abe, S., and N. Suzuki. "Complex-network description of seismicity." Nonlinear Processes in Geophysics 13, no. 2 (May 9, 2006): 145–50. http://dx.doi.org/10.5194/npg-13-145-2006.

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Abstract. The seismic data taken in California and Japan are mapped to growing random networks. It is shown in the undirected network picture that these earthquake networks are scale-free and small-work networks with the power-law connectivity distributions, the large values of the clustering coefficient, and the small values of the average path length. It is demonstrated how the present network approach reveals complexity of seismicity in a novel manner.

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Guo, Dong Wei, Xiang Yan Meng, and Cai Fang Hou. "Building Complex Network Similar to Facebook." Applied Mechanics and Materials 513-517 (February 2014): 909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.909.

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Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.

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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Fang, Yimeng. "Robustness analysis of highway network based on complex network." Highlights in Science, Engineering and Technology 42 (April 7, 2023): 291–97. http://dx.doi.org/10.54097/hset.v42i.7108.

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Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network efficiency, turn rate and robustness r. The results show that Chongqing and Shaanxi have a good performance of highway network robustness, Shandong and Sichuan have a more balanced performance, and Hunan has a weak performance. Enhance network robustness by placing route planning directions in place with fewer route options to provide drivers with more route options.

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Sun, Yan, Haixing Zhao, Jing Liang, and Xiujuan Ma. "Eigenvalue-based entropy in directed complex networks." PLOS ONE 16, no. 6 (June 21, 2021): e0251993. http://dx.doi.org/10.1371/journal.pone.0251993.

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Entropy is an important index for describing the structure, function, and evolution of network. The existing research on entropy is primarily applied to undirected networks. Compared with an undirected network, a directed network involves a special asymmetric transfer. The research on the entropy of directed networks is very significant to effectively quantify the structural information of the whole network. Typical complex network models include nearest-neighbour coupling network, small-world network, scale-free network, and random network. These network models are abstracted as undirected graphs without considering the direction of node connection. For complex networks, modeling through the direction of network nodes is extremely challenging. In this paper, based on these typical models of complex network, a directed network model considering node connection in-direction is proposed, and the eigenvalue entropies of three matrices in the directed network is defined and studied, where the three matrices are adjacency matrix, in-degree Laplacian matrix and in-degree signless Laplacian matrix. The eigenvalue-based entropies of three matrices are calculated in directed nearest-neighbor coupling, directed small world, directed scale-free and directed random networks. Through the simulation experiment on the real directed network, the result shows that the eigenvalue entropy of the real directed network is between the eigenvalue entropy of directed scale-free network and directed small-world network.

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Wang, Lifu, Yali Zhang, Jingxiao Han, and Zhi Kong. "Quantitative Controllability Index of Complex Networks." Advances in Mathematical Physics 2018 (October 22, 2018): 1–9. http://dx.doi.org/10.1155/2018/2586536.

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In this paper, the controllability issue of complex network is discussed. A new quantitative index using knowledge of control centrality and condition number is constructed to measure the controllability of given networks. For complex networks with different controllable subspace dimensions, their controllability is mainly determined by the control centrality factor. For the complex networks that have the equal controllable subspace dimension, their different controllability is mostly determined by the condition number of subnetworks’ controllability matrix. Then the effect of this index is analyzed based on simulations on various types of network topologies, such as ER random network, WS small-world network, and BA scale-free network. The results show that the presented index could reflect the holistic controllability of complex networks. Such an endeavour could help us better understand the relationship between controllability and network topology.

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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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Xiao, Wen Hong, and Xiang Dong Cai. "A Novel Wireless Sensor Network Model Based on Complex Network Theory." Advanced Materials Research 546-547 (July 2012): 1276–82. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1276.

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The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wireless sensor networks, in order to achieve certain characteristics of some wireless sensor networks to optimize the network. Considering multi-hop transmission of sensor network, this paper has proposed a maximum restriction on the communication radius of each sensor node; in order to improve the efficiency of energy consumption and maintain the sparsely of the entire network, this paper has also added a minimum restriction on the communication radius of each sensor node to the improved model; to balance the energy consumption of the entire network, The simulation results show that proposed improvements to the entire network more robust to random failure and energy costs are more balanced and reasonable. This is more applicable to wireless sensor networks.

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TEIXEIRA, G. M., M. S. F. AGUIAR, C. F. CARVALHO, D. R. DANTAS, M. V. CUNHA, J. H. M. MORAIS, H. B. B. PEREIRA, and J. G. V. MIRANDA. "COMPLEX SEMANTIC NETWORKS." International Journal of Modern Physics C 21, no. 03 (March 2010): 333–47. http://dx.doi.org/10.1142/s0129183110015142.

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Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences 18, no. 11 (November 20, 2014): 4565–78. http://dx.doi.org/10.5194/hess-18-4565-2014.

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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous US are studied. The connections in this streamflow network are examined primarily using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity, and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.

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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 2, 2014): 7255–89. http://dx.doi.org/10.5194/hessd-11-7255-2014.

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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, and in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous United States are studied. The connections in this streamflow network are examined using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.

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Šubelj, Lovro. "Convex skeletons of complex networks." Journal of The Royal Society Interface 15, no. 145 (August 2018): 20180422. http://dx.doi.org/10.1098/rsif.2018.0422.

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A convex network can be defined as a network such that every connected induced subgraph includes all the shortest paths between its nodes. A fully convex network would therefore be a collection of cliques stitched together in a tree. In this paper, we study the largest high-convexity part of empirical networks obtained by removing the least number of edges, which we call a convex skeleton. A convex skeleton is a generalization of a network spanning tree in which each edge can be replaced by a clique of arbitrary size. We present different approaches for extracting convex skeletons and apply them to social collaboration and protein interactions networks, autonomous systems graphs and food webs. We show that the extracted convex skeletons retain the degree distribution, clustering, connectivity, distances, node position and also community structure, while making the shortest paths between the nodes largely unique. Moreover, in the Slovenian computer scientists coauthorship network, a convex skeleton retains the strongest ties between the authors, differently from a spanning tree or high-betweenness backbone and high-salience skeleton. A convex skeleton thus represents a simple definition of a network backbone with applications in coauthorship and other social collaboration networks.

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Zhang, Guohua, Zhen Li, and Qiaoli Zhang. "Controllability of Complex Power Networks." Network and Communication Technologies 3, no. 1 (November 24, 2017): 1. http://dx.doi.org/10.5539/nct.v3n1p1.

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With the progress of time, the power network has been the basis of economic development. However, people have little knowledge of the controllability of the power network. This article will study eight power networks and compare the controllability of the power network in many aspects.

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Chen, Yangyang, Yi Zhao, and Xinyu Han. "Characterization of Symmetry of Complex Networks." Symmetry 11, no. 5 (May 20, 2019): 692. http://dx.doi.org/10.3390/sym11050692.

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Recently, symmetry in complex network structures has attracted some research interest. One of the fascinating problems is to give measures of the extent to which the network is symmetric. In this paper, based on the natural action of the automorphism group Aut ( Γ ) of Γ on the vertex set V of a given network Γ = Γ ( V , E ) , we propose three indexes for the characterization of the global symmetry of complex networks. Using these indexes, one can get a quantitative characterization of how symmetric a network is and can compare the symmetry property of different networks. Moreover, we compare these indexes to some existing ones in the literature and apply these indexes to real-world networks, concluding that real-world networks are far from vertex symmetric ones.

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Gong, Yuhui, and Qian Yu. "Evolution of Conformity Dynamics in Complex Social Networks." Symmetry 11, no. 3 (February 28, 2019): 299. http://dx.doi.org/10.3390/sym11030299.

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Conformity is a common phenomenon among people in social networks. In this paper, we focus on customers’ conformity behaviors in a symmetry market where customers are located in a social network. We establish a conformity model and analyze it in ring network, random network, small-world network, and scale-free network. Our simulations shown that topology structure, network size, and initial market share have significant effects on the evolution of customers’ conformity behaviors. The market will likely converge to a monopoly state in small-world networks but will form a duopoly market in scale networks. As the size of the network increases, there is a greater possibility of forming a dominant group of preferences in small-world network, and the market will converge to the monopoly of the product which has the initial selector in the market. Also, network density will become gradually significant in small-world networks.

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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

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唐, 绍明. "Research on Establishing Network Algorithm of Complex Networks." Computer Science and Application 12, no. 06 (2022): 1559–63. http://dx.doi.org/10.12677/csa.2022.126156.

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Sultan, Husam, and Basim Mahmood. "Analyzing Crime Networks: A Complex Network-Based Approach." AL-Rafidain Journal of Computer Sciences and Mathematics 15, no. 1 (June 1, 2021): 57–73. http://dx.doi.org/10.33899/csmj.2021.168261.

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Jalili, Mahdi. "Network biology: Describing biological systems by complex networks." Physics of Life Reviews 24 (March 2018): 159–61. http://dx.doi.org/10.1016/j.plrev.2017.12.003.

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Milo, R. "Network Motifs: Simple Building Blocks of Complex Networks." Science 298, no. 5594 (October 25, 2002): 824–27. http://dx.doi.org/10.1126/science.298.5594.824.

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Lynn, Christopher W., and Danielle S. Bassett. "Quantifying the compressibility of complex networks." Proceedings of the National Academy of Sciences 118, no. 32 (August 4, 2021): e2023473118. http://dx.doi.org/10.1073/pnas.2023473118.

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Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding—or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization—which is characterized by modular structure and heterogeneous degrees—facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network’s structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

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Wang, Zhi Kun. "Application of Complex Network Theory in Computer Network Topology Optimization Research." Advanced Materials Research 989-994 (July 2014): 4237–40. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4237.

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If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

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Ni, Yan, Yinghua Wang, Tao Yu, and Xiaoli Li. "Analysis of Epileptic Seizures with Complex Network." Computational and Mathematical Methods in Medicine 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/283146.

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Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple channel EEG recordings based on regular, small world, random, and scale-free network models. Whether the connection patterns of cortical networks are directly associated with the epileptic seizures was investigated. The results showed that small world and scale-free cortical networks are highly correlated with the occurrence of epileptic seizures. In particular, the property of small world network is more significant during the epileptic seizures.

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WU, ZHAOYAN, and XINCHU FU. "SYNCHRONIZATION OF COMPLEX-VARIABLE DYNAMICAL NETWORKS WITH COMPLEX COUPLING." International Journal of Modern Physics C 24, no. 02 (February 2013): 1350007. http://dx.doi.org/10.1142/s0129183113500071.

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In this paper, synchronization of complex-variable dynamical networks with complex coupling is investigated. An adaptive feedback control scheme is adopted to design controllers for achieving synchronization of a general network with both complex inner and outer couplings. For a network with only complex inner or outer coupling, pinning control and adaptive coupling strength methods are adopted to achieve synchronization under some assumptions. Several synchronization criteria are derived based on Lyapunov stability theory. Numerical simulations are provided to verify the effectiveness of the theoretical results.

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MARC, TILEN, and LOVRO ŠUBELJ. "Convexity in complex networks." Network Science 6, no. 2 (February 6, 2018): 176–203. http://dx.doi.org/10.1017/nws.2017.37.

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AbstractMetric graph properties lie in the heart of the analysis of complex networks, while in this paper we study their convexity through mathematical definition of a convex subgraph. A subgraph is convex if every geodesic path between the nodes of the subgraph lies entirely within the subgraph. According to our perception of convexity, convex network is such in which every connected subset of nodes induces a convex subgraph. We show that convexity is an inherent property of many networks that is not present in a random graph. Most convex are spatial infrastructure networks and social collaboration graphs due to their tree-like or clique-like structure, whereas the food web is the only network studied that is truly non-convex. Core–periphery networks are regionally convex as they can be divided into a non-convex core surrounded by a convex periphery. Random graphs, however, are only locally convex meaning that any connected subgraph of size smaller than the average geodesic distance between the nodes is almost certainly convex. We present different measures of network convexity and discuss its applications in the study of networks.

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Chung, Daewon, and Insoo Sohn. "Neural Network Optimization Based on Complex Network Theory: A Survey." Mathematics 11, no. 2 (January 7, 2023): 321. http://dx.doi.org/10.3390/math11020321.

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Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.

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LEYVA, I., I. SENDIÑA-NADAL, J. A. ALMENDRAL, J. M. BULDÚ, D. LI, S. HAVLIN, and S. BOCCALETTI. "ENTRAINMENT COMPETITION IN COMPLEX NETWORKS." International Journal of Bifurcation and Chaos 20, no. 03 (March 2010): 827–33. http://dx.doi.org/10.1142/s0218127410026113.

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The response of a random and modular network to the simultaneous presence of two frequencies is considered. The competition for controlling the dynamics of the network results in different behaviors, such as frequency changes or permanent synchronization frustration, which can be directly related to the network structure. From these observations, we propose a new method for detecting overlapping communities in structured networks.

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Cui, Lai Zhon, Nan Lu, and Yuan Yuan Jin. "Community Clustering Algorithm on Semantic Similarity in Complex Network." Lecture Notes on Software Engineering 2, no. 4 (2014): 348–52. http://dx.doi.org/10.7763/lnse.2014.v2.148.

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Zhang, Zhi-hua, En-ke Hou, and Xiao xia Luo. "Integration Method of Three-dimensional Complex Tunnel Network Model." International Journal of Engineering and Technology 3, no. 5 (2011): 533–39. http://dx.doi.org/10.7763/ijet.2011.v3.281.

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Xinyi Wang, Xinyi Wang, Jinji Fu Xinyi Wang, Rui Qi Jinji Fu, Bokai Xu Rui Qi, and Ming Huang Bokai Xu. "College Students Service Feedback Based on a Complex Network." 網際網路技術學刊 23, no. 6 (November 2022): 1297–302. http://dx.doi.org/10.53106/160792642022112306012.

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<p>The ideas of others always influence people because they are social animals. They will evaluate a movie based on the rating level and change their decision based on someone’s advice. It is expected that the comments on the news are reversed suddenly because of a few people, especially in the context of the communication wave set off by the Internet as a new media. It is worth noting that there is a relationship between the deviation of public opinion and the intimacy between people, and confidence and openness also play a role. Recently, there has been renewed interest in dynamic models of research opinions. Our goal is to build a dynamic model of opinion offset based on various influencing factors and then use it to control public opinion more accurately and reduce the loss caused by them. We analyzed existing models and found that few articles considered people’s confidence, openness, and intimacy together. Therefore, we designed new models that considered all the influencing factors. We tested the model with actual data and achieved high accuracy. Finally, we found that opinions would eventually converge to a peak value, and the time needed for convergence was affected by intimacy, openness, and confidence.</p> <p> </p>

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Taluja, Anuradha, A. K. Solanki, and Harish Kumar. "A comprehensive approach for assessing the reliability of complex networks using OANN approach." Multidisciplinary Science Journal 5 (August 10, 2023): 2023ss0104. http://dx.doi.org/10.31893/multiscience.2023ss0104.

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The optimistic model of network reliability assumes the nodes to be trustworthy while assigning a failure probability to the network's connections. For calculating a network's reliability, the persistent condition is used to guarantee that no two edges have the same failure probability. Accurately estimating the network's reliability across all of its endpoints is an NP-hard problem. The study of network reliability centers on problems arising from topological isolation of individual data nodes. Analysis of network reliability spans the phases of building, deploying, and testing a network. Issues with connection, capacity, and trip time are all metrics used in analyses of computer network reliability. In this paper, an Optimal ANN strategy is proposed as a means of assessing the reliability of the network. In this research, we analyze the methods used in and findings from recent studies on trustworthiness. The term "Optimized Artificial Neural Network" (OANN) is used to describe a strategy that takes into account aspects of both neural networks and another way for measuring trustworthiness. Results are well tested on network of 2^8nodes (mesh network) and hyper-tree network of n nodes. A network's performance may be evaluated with the help of the suggested method, which also contributes to the calculation of its cost and reliability metrics.

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Zhang, Hao, Di-Yi Chen, Bei-Bei Xu, and Run-Fan Zhang. "Controllability of fractional-order directed complex networks." Modern Physics Letters B 28, no. 27 (October 20, 2014): 1450211. http://dx.doi.org/10.1142/s021798491450211x.

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This paper is a step forward to generalize the fundamentals of the conventional controllability in fractional-order complex networks. First, we discuss the existence of controllability theory of fractional-order complex networks. Furthermore, we propose stringent mathematical expression and controllable proof of fractional complex networks. Finally, three typical examples from the simplest network, the chain fractional-order network, to the Small-World network are presented to validate the correctness of the above theorem.

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Tam, W. M., F. C. M. Lau, and C. K. Tse. "Complex-Network Modeling of a Call Network." IEEE Transactions on Circuits and Systems I: Regular Papers 56, no. 2 (February 2009): 416–29. http://dx.doi.org/10.1109/tcsi.2008.925947.

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Xin, Ruyue, Jiang Zhang, and Yitong Shao. "Complex network classification with convolutional neural network." Tsinghua Science and Technology 25, no. 4 (August 2020): 447–57. http://dx.doi.org/10.26599/tst.2019.9010055.

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Small, Michael, Lvlin Hou, and Linjun Zhang. "Random complex networks." National Science Review 1, no. 3 (July 18, 2014): 357–67. http://dx.doi.org/10.1093/nsr/nwu021.

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Abstract Exactly what is meant by a ‘complex’ network is not clear; however, what is clear is that it is something other than a random graph. Complex networks arise in a wide range of real social, technological and physical systems. In all cases, the most basic categorization of these graphs is their node degree distribution. Particular groups of complex networks may exhibit additional interesting features, including the so-called small-world effect or being scale-free. There are many algorithms with which one may generate networks with particular degree distributions (perhaps the most famous of which is preferential attachment). In this paper, we address what it means to randomly choose a network from the class of networks with a particular degree distribution, and in doing so we show that the networks one gets from the preferential attachment process are actually highly pathological. Certain properties (including robustness and fragility) which have been attributed to the (scale-free) degree distribution are actually more intimately related to the preferential attachment growth mechanism. We focus here on scale-free networks with power-law degree sequences—but our methods and results are perfectly generic.

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Yeqing, Zhao. "Knowledge Evolution of Complex Agent Networks." MATEC Web of Conferences 173 (2018): 03050. http://dx.doi.org/10.1051/matecconf/201817303050.

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

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Yeqing, Zhao. "Knowledge Evolution of Complex Agent Networks." MATEC Web of Conferences 176 (2018): 03007. http://dx.doi.org/10.1051/matecconf/201817603007.

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

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Sun, Gengxin, Chih-Cheng Chen, and Sheng Bin. "Study of Cascading Failure in Multisubnet Composite Complex Networks." Symmetry 13, no. 3 (March 23, 2021): 523. http://dx.doi.org/10.3390/sym13030523.

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Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affect the process and scale of the network cascading failure. In this paper, a composite network is constructed based on the multisubnet composite complex network model, and its cascading failure is proposed combined with multiple relationships. The effect of intranetwork relationships and coupling relationships on network robustness under different influencing factors is studied. It is shown that cascading failure in composite networks is different from coupling networks, and increasing the strength of the coupling relationship can significantly improve the robustness of the network.

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Guo, Rongchun. "News Hotspot Event Diffusion Mechanism Based on Complex Network." Mathematical Problems in Engineering 2022 (May 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/1455324.

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The wide range of social hot news events on the Internet has made the Internet have a great impact on the public. However, there are few studies on Internet information. In order to improve the efficiency of user network information dissemination of Internet information based on complex network theory and model simulation, this paper makes a more in-depth study on information dissemination on the Internet, constructs a complex network of Internet information dissemination, and analyzes the static topology and dynamic evolution process of the network. Using the attention relationship between Internet users, the Internet information dissemination network, degree, and path were used to select multiple indicators. The static topology of the network is analyzed by using the complex network theory. The study found that the complex network of Internet information is different from other complex networks. The influencing factors of network dynamic evolution are studied from three aspects: overall structure, local structure, and time constraints. The evolution trend of different forms and overall network nodes in the evolution process was explored, and the network dynamic evolution process model was constructed. Practice shows that the model can better describe the evolution process of network information dissemination in complex networks. The degree values of the two networks are positively correlated with the corresponding average clustering coefficients, and the networks have a significant hierarchy. The correlation between news hot events and network nodes is not as good as users’ attention to the network.

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YANG, Bo, Da-You LIU, Jiming LIU, Di JIN, and Hai-Bin MA. "Complex Network Clustering Algorithms." Journal of Software 20, no. 1 (April 7, 2009): 54–66. http://dx.doi.org/10.3724/sp.j.1001.2009.00054.

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Abe, Sumiyoshi, and Norikazu Suzuki. "Complex Network of Seismicity." Progress of Theoretical Physics Supplement 162 (2006): 138–46. http://dx.doi.org/10.1143/ptps.162.138.

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Li, Chunguang, Xiaofeng Liao, and Juebang Yu. "Complex-valued wavelet network." Journal of Computer and System Sciences 67, no. 3 (November 2003): 623–32. http://dx.doi.org/10.1016/s0022-0000(03)00069-2.

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Motter, A. E., C. S. Zhou, and J. Kurths. "Enhancing complex-network synchronization." Europhysics Letters (EPL) 69, no. 3 (February 2005): 334–40. http://dx.doi.org/10.1209/epl/i2004-10365-4.

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Mitchell, Melanie. "Complex systems: Network thinking." Artificial Intelligence 170, no. 18 (December 2006): 1194–212. http://dx.doi.org/10.1016/j.artint.2006.10.002.

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Journal articles on the topic 'Complex network'

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