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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 (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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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (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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Asbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (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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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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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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Hernandez, Bryan S., Patrick Vincent N. Lubenia, Matthew D. Johnston, and Jae Kyoung Kim. "A framework for deriving analytic steady states of biochemical reaction networks." PLOS Computational Biology 19, no. 4 (2023): e1011039. http://dx.doi.org/10.1371/journal.pcbi.1011039.
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The long-term behaviors of biochemical systems are often described by their steady states. Deriving these states directly for complex networks arising from real-world applications, however, is often challenging. Recent work has consequently focused on network-based approaches. Specifically, biochemical reaction networks are transformed into weakly reversible and deficiency zero generalized networks, which allows the derivation of their analytic steady states. Identifying this transformation, however, can be challenging for large and complex networks. In this paper, we address this difficulty by breaking the complex network into smaller independent subnetworks and then transforming the subnetworks to derive the analytic steady states of each subnetwork. We show that stitching these solutions together leads to the the analytic steady states of the original network. To facilitate this process, we develop a user-friendly and publicly available package, COMPILES (COMPutIng anaLytic stEady States). With COMPILES, we can easily test the presence of bistability of a CRISPRi toggle switch model, which was previously investigated via tremendous number of numerical simulations and within a limited range of parameters. Furthermore, COMPILES can be used to identify absolute concentration robustness (ACR), the property of a system that maintains the concentration of particular species at a steady state regardless of any initial concentrations. Specifically, our approach completely identifies all the species with and without ACR in a complex insulin model. Our method provides an effective approach to analyzing and understanding complex biochemical systems.
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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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Koam, Ali N. A., Ali Ahmad, and Yasir Ahmad. "Computation of reverse degree-based topological indices of hex-derived networks." AIMS Mathematics 6, no. 10 (2021): 11330–45. http://dx.doi.org/10.3934/math.2021658.
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<abstract><p>Network theory gives an approach to show huge and complex frameworks through a complete arrangement of logical devices. A network is made is made of vertices and edges, where the degree of a vertex refers to the number of joined edges. The degree appropriation of a network represents the likelihood of every vertex having a particular degree and shows significant worldwide network properties. Network theory has applications in many disciplines like basic sciences, computer science, engineering, medical, business, public health and sociology. There are some important networks like logistical networks, gene regulatory networks, metabolic networks, social networks, derived networks. Topological index is a numerical number assigned to the molecular structure/netwrok which is used for correlation analysis in physical, theoretical and environmental chemistry. The hex-derived networks are created by hexagonal networks of dimension $ t $, these networks have an assortment of valuable applications in computer science, medical science and engineering. In this paper we discuss the reverse degree-based topological for third type of hex-derived networks.</p></abstract>
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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences 18, no. 11 (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 (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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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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Lu, WeiPing, DeCai Huo, and Sibo Jia. "Analysis of Key Factors of College Students’ Ideological and Political Education Based on Complex Network." Journal of Sensors 2022 (July 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/6577878.
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Rapid updating and complex network means bringing more development opportunities for the education industry. As for ideological classes and political science at colleges and universities, how to use sophisticated online technology to educate and educate ideological and political classes and improve the formation of ideological and political classes has been a complex theory in the education industry in recent years. Complex network is an abstract representation of complex systems in the real world, with broad research value and application prospects, and has many advantages in complex network research, with interpretability, expression ability, generalization ability, flexibility, etc., and has been used in various network analysis tasks, such as community discovery, link prediction, network representation, and political learning. The second part of this article focuses on (1) the concept of ideological education, (2) the advantages of ideological education, (3) the contradiction of the mode of ideological education, and (4) how to innovate ideological education. The third part proposes the basic characteristics and models of complex networks and proposes the emergence of fractal structures. The fourth part analyzes complex network models in detail and compares the number of points and sides of the real network with the respective irregular networks, indicating that the real world is not fully defined or completely irregular, and that the real network has the nature of a small world and a high-quality cluster. The key factors of complex networks applied propose theoretical and political education and ultimately influence theoretical and political teachers and students in the complex networks examined through empirical questionnaires. He proposes that large universities should be able to use a network environment to promote ideological and political education.
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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 (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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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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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (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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Sultan, Husam, and Basim Mahmood. "Analyzing Crime Networks: A Complex Network-Based Approach." AL-Rafidain Journal of Computer Sciences and Mathematics 15, no. 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 (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 (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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Gong, Yuhui, and Qian Yu. "Evolution of Conformity Dynamics in Complex Social Networks." Symmetry 11, no. 3 (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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Chen, Yangyang, Yi Zhao, and Xinyu Han. "Characterization of Symmetry of Complex Networks." Symmetry 11, no. 5 (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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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 (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&rsquo;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&rsquo;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>&nbsp;</p>
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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, et al. "COMPLEX SEMANTIC NETWORKS." International Journal of Modern Physics C 21, no. 03 (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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Abe, S., and N. Suzuki. "Complex-network description of seismicity." Nonlinear Processes in Geophysics 13, no. 2 (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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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 (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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Ren, Xiaolong, Zhenyu Zhang, and Zixvan Yu. "Analysis of the Complex Characteristics of the New York City Subway Network." Highlights in Science, Engineering and Technology 64 (August 21, 2023): 99–106. http://dx.doi.org/10.54097/hset.v64i.11252.
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Complex networks are important to the real world. Complex networks are instructive for the study of real networks such as metro networks, social networks and information networks. This paper study the New York City subway network and use complex network theory to research the static properties of subway complex network. Conclude that 79% of the nodes in the New York City subway network have degree 2, the network diameter is 59, more than 90% of the network nodes have a clustering coefficient of 0, the standard robustness of the network is 0.326, and the average shortest path length of the network is 18.38. This paper use deliberate attacks and random attacks on the network to study the dynamic Properties. The New York City subway network sites have good robustness against random attacks. In contrast, the network exhibits vulnerability when the maximum mesonumber nodes are targeted for deliberate attacks, and the entire network will be nearly paralyzed when more mesonumber nodes fail.
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MARC, TILEN, and LOVRO ŠUBELJ. "Convexity in complex networks." Network Science 6, no. 2 (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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Šubelj, Lovro. "Convex skeletons of complex networks." Journal of The Royal Society Interface 15, no. 145 (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, Chengjun, Yi Lei, Xinyu Shen, et al. "Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks." Entropy 25, no. 1 (2022): 22. http://dx.doi.org/10.3390/e25010022.
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The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network’s giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure.
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Zeng, Lang, Zhen Jia, and Yingying Wang. "Extraction algorithm for optimal coarse-grained networks on complex networks." International Journal of Modern Physics C 30, no. 11 (2019): 1950081. http://dx.doi.org/10.1142/s0129183119500815.
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Coarse-graining of complex networks is a hot topic in network science. Coarse-grained networks are required to preserve the topological information or dynamic properties of the original network. Some effective coarse-graining methods have been proposed, while an urgent problem is how to obtain coarse-grained network with optimal scale. In this paper, we propose an extraction algorithm (EA) for optimal coarse-grained networks. Numerical simulation for EA on four kinds of networks and performing Kuramoto model on optimal coarse-grained networks, we find our algorithm can effectively obtain the optimal coarse-grained network.
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Šimon, Marek, Iveta Dirgová Luptáková, Ladislav Huraj, Marián Hosťovecký, and Jiří Pospíchal. "Combined Heuristic Attack Strategy on Complex Networks." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6108563.
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Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.
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Jian, Feng, and Shi Dandan. "Complex Network Theory and Its Application Research on P2P Networks." Applied Mathematics and Nonlinear Sciences 1, no. 1 (2016): 45–52. http://dx.doi.org/10.21042/amns.2016.1.00004.
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AbstractAdvances in complex networks of Peer-to-Peer (P2P) networks were reviewed and summarized. The paper outlines some important topological properties such as degree, average path length and clustering coefficient at first, and then three kinds of most important network mechanism models are introduced, including random graph model, small world model and scale-free model. A simple description about research status for P2P networks based on complex networks is made from three aspects: positive research, network mechanism model, network broadcast and control. Some developing prospects of complex networks of P2P are pointed out finally. Complex network provides new ideas and methods to deal with many complex problems including P2P networks.
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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 (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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Xuan, Qi, Xiaodi Ma, Chenbo Fu, Hui Dong, Guijun Zhang, and Li Yu. "Heterogeneous multidimensional scaling for complex networks." International Journal of Modern Physics C 26, no. 02 (2015): 1550023. http://dx.doi.org/10.1142/s0129183115500230.
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Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.
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Li, Xin-Feng, Zhe-Ming Lu, and Hui Li. "Controllability of deterministic complex networks." International Journal of Modern Physics C 26, no. 03 (2015): 1550028. http://dx.doi.org/10.1142/s012918311550028x.
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Recently, the controllability of complex networks has attracted wide attention of researchers. The main contribution comes from Liu et al. who proposed the structural controllability as an analytical framework for making predictions regarding the control of directed networks in Nature. Since then, the controllability of many model and real networks has been deeply investigated except deterministic complex networks. In this paper, we focus on studying the controllability of deterministic complex networks. We examine six typical deterministic networks, the simulation results show that the minimum number of driver nodes grows linearly with network size. When the network size is large enough, the controllability approximates to a constant not more than 0.4, indicating that the deterministic networks are relatively easy to control. Furthermore, we investigate the characteristics of driver nodes in deterministic complex networks, finding that the driver nodes tend to avoid high degree nodes but to have high clustering coefficients.
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Small, Michael, Lvlin Hou, and Linjun Zhang. "Random complex networks." National Science Review 1, no. 3 (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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Pathak, Sanjai, Ashish Mani, Mayank Sharma, and Amlan Chatterjee. "Controller Placement in Vehicular Networks: A Novel Algorithm Utilizing Elite Opposition-Based Salp Swarm and an Adaptable Approach." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5 (2023): 223–32. http://dx.doi.org/10.17762/ijritcc.v11i5.6609.
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The rapid advancement of networking technology has enabled small devices to have communication capabilities, but the current decentralized communication system is not ideal for heterogeneous networks like vehicular networks. The integration of routing, switching, and decision-making capabilities in the same network device limits innovation and impedes performance in decentralized networks, especially in vehicular networks where network topologies change frequently. To address the demands of such networks, Software-Defined Networking (SDN) provides a promising solution that supports innovation. However, SDN's single-controller-based system may restrict the network's operational capabilities, despite being programmable and flexible. This paper suggests two methods to tackle the complex problem of controller placement in SDN: an adaptable approach based on OpenFlow protocol in OpenNet and an evolutionary algorithm called Elite Opposition-Based Salp Swarm Algorithm (EO-SSA) to minimize propagation latency, load imbalance, and network resilience. Multiple controllers increase the network's capabilities and provide fault tolerance, but their placement requires a trade-off among various objectives. The proposed methods have been evaluated and analyzed to confirm their effectiveness. The current decentralized network system is not adequate for vehicular networks, and SDN offers a promising solution that supports innovation and can meet the current demands of such networks.
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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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Yan, Houyi, Lvlin Hou, Yunxiang Ling, and Guohua Wu. "Optimizing complex networks controllability by local structure information." International Journal of Modern Physics C 27, no. 10 (2016): 1650115. http://dx.doi.org/10.1142/s0129183116501151.
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Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure information, this strategy is potentially more practical.
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Zhang, Guohua, Zhen Li, and Qiaoli Zhang. "Controllability of Complex Power Networks." Network and Communication Technologies 3, no. 1 (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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LI, KUN, XIAOFENG GONG, SHUGUANG GUAN, and C. H. LAI. "ANALYSIS OF TRAFFIC FLOW ON COMPLEX NETWORKS." International Journal of Modern Physics B 25, no. 10 (2011): 1419–28. http://dx.doi.org/10.1142/s0217979211100655.
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We propose a new routing strategy for controlling packet routing on complex networks. The delivery capability of each node is adopted as a piece of local information to be integrated with the load traffic dynamics to weight the next route. The efficiency of transport on complex network is measured by the network capacity, which is enhanced by distributing the traffic load over the whole network while nodes with high handling ability bear relative heavier traffic burden. By avoiding the packets through hubs and selecting next routes optimally, most travel times become shorter. The simulation results show that the new strategy is not only effective for scale-free networks but also for mixed networks in realistic networks.
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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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Li, Faxu, Liang Wei, Haixing Zhao, and Feng Hu. "On the Distributions of Subgraph Centralities in Complex Networks." Discrete Dynamics in Nature and Society 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/473248.
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Subgraph centrality measure characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than large ones, which makes this measure appropriate for characterizing network motifs. This measure is better in being able to discriminate the nodes of a network than alternate measures. In this paper, the important issue of subgraph centrality distributions is investigated through theory-guided extensive numerical simulations, for three typical complex network models, namely, the ER random-graph networks, WS small-world networks, and BA scale-free networks. It is found that these three very different types of complex networks share some common features, particularly that the subgraph centrality distributions in increasing order are all insensitive to the network connectivity characteristics, and also found that the probability distributions of subgraph centrality of the ER and of the WS models both follow the gamma distribution, and the BA scale-free networks exhibit a power-law distribution with an exponential cutoff.
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Cai, Hongyun, Xiaomei Gong, and Jianlei Han. "Analysis on the Spatial Structure and Interaction of Aviation Network and Tourism Efficiency Network in Major Cities in China." Academic Journal of Management and Social Sciences 2, no. 1 (2023): 134–45. http://dx.doi.org/10.54097/ajmss.v2i1.6504.
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Tourism efficiency is crucial for measuring sustainable tourism development. Examining the relationship between aviation and tourism efficiency networks is key to promoting their synergistic development in China's urban areas. This study employs various methods, such as complex network analysis method, entropy-weighted TOPSIS, tourism efficiency gravity model, and quadratic assignment procedure, to analyze the networks' spatial structure evolution characteristics and interaction effects. Results show that (1) China's major cities' aviation network has improved its organizational efficiency and formed a "double rhombus-single axis" spatial evolution pattern of the axis-spoke network. The number of intermediary networks and hub cities in the central and western regions has increased. (2) The tourism efficiency network adopts a "honeycomb" structure pattern with the simultaneous layout of "point-to-point" and "star" networks. The network's tourism efficiency follows "Pareto's Law," and tourism cities above the second level form a club group development. The tourism efficiency development potential area is shifting to the southwest. (3) The aviation and tourism efficiency networks exhibit a clear trend of synergistic evolution with a "path locking" phenomenon between them. Differences in tourism resource endowment, labor advantage, and capital advantage positively impact the aviation network's structure. Conversely, differences in revenue capacity and market scale negatively impact the structure. The aviation scale advantage, openness, intimacy, and influence exhibit decreasing positive effects on the tourism efficiency network's structure.
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Adami, Vivian Sebben, and Jorge Renato Verschoore. "Implications of Network Relations for the Governance of Complex Projects." Project Management Journal 49, no. 2 (2018): 71–88. http://dx.doi.org/10.1177/875697281804900205.
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Our article aims to answer the call for studies on new perspectives of complex projects and their governance. We adopt the social network approach to investigate the implications of network relations for the governance of project networks. We analyze quantitative and qualitative data following two theoretical models: flow and coordination. Our results show how the supply, contractual, and information networks influence the governance of project networks. We contribute to the literature explaining the dependence of the project network governance to network relations. It is necessary to use different theoretical models to analyze the coordination and control of complex project networks.
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Liu, Yongshan, Jianjun Wang, Haitao He, Guoyan Huang, and Weibo Shi. "Identifying important nodes affecting network security in complex networks." International Journal of Distributed Sensor Networks 17, no. 2 (2021): 155014772199928. http://dx.doi.org/10.1177/1550147721999285.
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An important node identification algorithm based on an improved structural hole and K-shell decomposition algorithm is proposed to identify important nodes that affect security in complex networks. We consider the global structure of a network and propose a network security evaluation index of important nodes that is free of prior knowledge of network organization based on the degree of nodes and nearest neighborhood information. A node information control ability index is proposed according to the structural hole characteristics of nodes. An algorithm ranks the importance of nodes based on the above two indices and the nodes’ local propagation ability. The influence of nodes on network security and their own propagation ability are analyzed by experiments through the evaluation indices of network efficiency, network maximum connectivity coefficient, and Kendall coefficient. Experimental results show that the proposed algorithm can improve the accuracy of important node identification; this analysis has applications in monitoring network security.