Статті в журналах: "Evolution on networks"

1

Huang, Ailing, Jie Xiong, Jinsheng Shen, and Wei Guan. "Evolution of weighted complex bus transit networks with flow." International Journal of Modern Physics C 27, no. 06 (May 13, 2016): 1650064. http://dx.doi.org/10.1142/s0129183116500649.

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Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

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2

Liu, Yaqin, Yunsi Chen, Qing He, and Qian Yu. "Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective." Systems 11, no. 2 (February 5, 2023): 82. http://dx.doi.org/10.3390/systems11020082.

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.

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3

Manrubia, Susanna C., and José A. Cuesta. "Neutral networks of genotypes: evolution behind the curtain." Arbor 186, no. 746 (December 30, 2010): 1051–64. http://dx.doi.org/10.3989/arbor.2010.746n1253.

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4

Walker, David M., Antoinette Tordesillas, Amy L. Rechenmacher, and Michael Small. "Multiscale resolution of networks of granular media network evolution—a network of networks." IEICE Proceeding Series 2 (March 17, 2014): 294–97. http://dx.doi.org/10.15248/proc.2.294.

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5

Tupikina, L., K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan, and J. Kurths. "Characterizing the evolution of climate networks." Nonlinear Processes in Geophysics 21, no. 3 (June 25, 2014): 705–11. http://dx.doi.org/10.5194/npg-21-705-2014.

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Abstract. Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős–Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970–2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.

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6

Besjedica, Toni, Krešimir Fertalj, Vlatko Lipovac, and Ivona Zakarija. "Evolution of Hybrid LiFi–WiFi Networks: A Survey." Sensors 23, no. 9 (April 25, 2023): 4252. http://dx.doi.org/10.3390/s23094252.

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Given the growing number of devices and their need for internet access, researchers are focusing on integrating various network technologies. Concerning indoor wireless services, a promising approach in this regard is to combine light fidelity (LiFi) and wireless fidelity (WiFi) technologies into a hybrid LiFi and WiFi network (HLWNet). Such a network benefits from LiFi’s distinct capability for high-speed data transmission and from the wide radio coverage offered by WiFi technologies. In this paper, we describe the framework for the HWLNet architecture, providing an overview of the handover methods used in HLWNets and presenting the basic architecture of hybrid LiFi/WiFi networks, optimization of cell deployment, relevant modulation schemes, illumination constraints, and backhaul device design. The survey also reviews the performance and recent achievements of HLWNets compared to legacy networks with an emphasis on signal to noise and interference ratio (SINR), spectral and power efficiency, and quality of service (QoS). In addition, user behaviour is discussed, considering interference in a LiFi channel is due to user movement, handover frequency, and load balancing. Furthermore, recent advances in indoor positioning and the security of hybrid networks are presented, and finally, directions of the hybrid network’s evolution in the foreseeable future are discussed.

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7

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

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This paper proposes a weighted clique evolution model based on clique (maximal complete subgraph) growth and edge-weight driven for complex networks. The model simulates the scheme of real-world networks that the evolution of networks is likely to be driven by the flow, such as traffic or information flow needs, as well as considers that real-world networks commonly consist of communities. At each time step of a network’s evolution progress, an edge is randomly selected according to a preferential scheme. Then a new clique which contains the edge is added into the network while the weight of the edge is adjusted to simulate the flow change brought by the new clique addition. We give the theoretical analysis based on the mean field theory, as well as some numerical simulation for this model. The result shows that the model can generate networks with scale-free distributions, such as edge weight distribution and node strength distribution, which can be found in many real-world networks. It indicates that the evolution rule of the model may attribute to the formation of real-world networks.

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8

Pachón, Alvaro. "Networks Architecture Evolution." Sistemas y Telemática 1, no. 1 (July 28, 2006): 77. http://dx.doi.org/10.18046/syt.v1i1.1079.

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9

Dorogovtsev, S. N., and J. F. F. Mendes. "Evolution of networks." Advances in Physics 51, no. 4 (June 2002): 1079–187. http://dx.doi.org/10.1080/00018730110112519.

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10

Tian, Yang, Guoqi Li, and Pei Sun. "Information evolution in complex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073105. http://dx.doi.org/10.1063/5.0096009.

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Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual–individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but noise accounts for disturbing them. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks.

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11

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 (March 27, 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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12

Kleinbaum, Adam M., and Madeline King Kneeland. "Network Evolution: Exploring the Dynamics of Organizational Networks." Academy of Management Proceedings 2018, no. 1 (August 2018): 17069. http://dx.doi.org/10.5465/ambpp.2018.17069symposium.

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13

Zhang, Rui, Xiaomeng Wang, Ming Cheng, and Tao Jia. "The evolution of network controllability in growing networks." Physica A: Statistical Mechanics and its Applications 520 (April 2019): 257–66. http://dx.doi.org/10.1016/j.physa.2019.01.042.

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14

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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15

KLEIN, LEANDER LUIZ, and BRENO AUGUSTO DINIZ PEREIRA. "Interdependência entre redes e empresas integrantes na evolução de redes interorganizacionais." Cadernos EBAPE.BR 17, spe (November 2019): 732–49. http://dx.doi.org/10.1590/1679-395174636.

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Abstract Cooperation among firms through networks is a form to obtain a variety of resources. Over time, networks can become an exclusive provider of some resources used by members, leading to dependencys. This study is interested in this phenomenon, and aims to examine how firm-network interdependence grows throughout the development of inter-organizational networks. The research was conducted with networks that were in distinct stages of evolution (formation, development, and professionalization). Interviews were carried out with the presidents of the networks and two member firms of each network. The study identified an inversion in the relation of interdependence investigated, where the network is dependent of its members in the first stages of evolution and, as its governance and structure consolidate, members develop a dependency relationship toward the network and the benefits it offers.

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16

Belyakin, Sergey, and Sergey Shuteev. "Classical Soliton Theory for Studying the Dynamics and Evolution of in Network." Journal of Clinical Research and Reports 08, no. 04 (July 31, 2021): 01–06. http://dx.doi.org/10.31579/2690-1919/186.

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This paper presents the dynamic model ofthe soliton. Based on this model, it is supposed to study the state of the network. The term neural networks refersto the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons.

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17

Singh, Kirti, and Poonam Yadav. "Performance Evolution of Intrusion Detection system on MANET Using Genetic Evolution." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 3 (November 30, 2012): 351–53. http://dx.doi.org/10.24297/ijct.v3i3a.2937.

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Mobile ad hoc networks (MANETs) are one of the best ever growing areas of research. By providing communications in the absence of fixed infrastructure MANETs are an attractive technology. However this edibility introduces new security threats. The traditional way of protecting networks is not directly applicable to MANETs. Many conventional security solutions are ineffective and inefficient for the highly dynamic and resource-constrained environments where MANET use might be expected.Â In this paper we solving security issue in Mobile Adhoc Network using Evolutionary Computation that will be discover complex properties of mobile ad hoc networks and evolve intrusion detection programs suitable for this new environment. Programs evolved using Grammatical Evolution techniques which is part of Evolutionary Computation, will be able to detect specific routing attacks on mobile ad hoc networks.

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18

Aouad, Siham, Issam El Meghrouni, Yassine Sabri, Adil Hilmani, and Abderrahim Maizate. "Security of software defined networks: evolution and challenges." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (November 1, 2023): 384. http://dx.doi.org/10.11591/ijres.v12.i3.pp384-391.

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<span>In software-defined networking (SDN), network traffic is managed by software controllers or application programming interfaces (APIs) rather than hardware components. It differs from traditional networks, which use switches and routers to control traffic. Using SDN, you can create and control virtual networks or traditional hardware networks. Furthermore, OpenFlow allows network administrators to control exact network behavior through centralized control of packet forwarding. For these reasons, SDN has advantages over certain security issues, unlike traditional networks. However, most of the existing vulnerabilities and security threats in the traditional network also impact the SDN network. This document presents the attacks targeting the SDN network and the solutions that protect against these attacks. In addition, we introduce a variety of SDN security controls, such as intrusion detection systems (IDS)/intrusion prevention system (IPS), and firewalls. Towards the end, we outline a conclusion and perspectives.</span>

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19

Kong, Yuanbo, and Rui Wu. "Evolution of Scholar Networks During the COVID-19 Outbreak." Advances in Engineering Technology Research 5, no. 1 (May 6, 2023): 355. http://dx.doi.org/10.56028/aetr.5.1.355.2023.

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This paper reviews the academic collaboration network in COVID-19 research from January 2020 to December 2021. We first collected publication data from the Covidia (https://covidia.acemap.info) and constructed a coauthorship network. Then, we select network nodes ranking algorithms on importance from three aspects: information transmission in the network, network modularization, and network stability. Through community investigation and node ranking, we analyzed the evolving characteristics of the COVID-19 collaboration network, including the weakening of overall collaboration density, the strengthening of the network modularization trend, a more uniform distribution of researchers' influence, and the network's adaptation to major events such as international cooperation and the emergence of variant strains. Additionally, we investigated the sensitivity of different indices to international cooperation and variant strains, as well as the time delay between major COVID-19-related events and changes in network indices. This analysis provides insights into the dynamics of academic collaboration networks during the pandemic and can be used as a reference for future research in the field, including the effectiveness of different collaboration models and the potential of network analysis in identifying and predicting emerging infectious diseases.

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20

Meng, Yangyang, Xiaofei Zhao, Jianzhong Liu, and Qingjie Qi. "Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network." Sustainability 15, no. 12 (June 8, 2023): 9309. http://dx.doi.org/10.3390/su15129309.

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With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.

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21

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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22

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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23

AOYAMA, TOMONORI. "Evolution of Photonic Networks." Journal of the Institute of Electrical Engineers of Japan 121, no. 9 (2001): 598–603. http://dx.doi.org/10.1541/ieejjournal.121.598.

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24

Kramer, S., and M. Marder. "Evolution of river networks." Physical Review Letters 68, no. 2 (January 13, 1992): 205–8. http://dx.doi.org/10.1103/physrevlett.68.205.

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25

Vachaspati, Tanmay, and Alexander Vilenkin. "Evolution of cosmic networks." Physical Review D 35, no. 4 (February 15, 1987): 1131–37. http://dx.doi.org/10.1103/physrevd.35.1131.

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26

Christensen, Kim, Raul Donangelo, Belita Koiller, and Kim Sneppen. "Evolution of Random Networks." Physical Review Letters 81, no. 11 (September 14, 1998): 2380–83. http://dx.doi.org/10.1103/physrevlett.81.2380.

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27

Menczer, F. "Evolution of document networks." Proceedings of the National Academy of Sciences 101, Supplement 1 (January 27, 2004): 5261–65. http://dx.doi.org/10.1073/pnas.0307554100.

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28

Fitch, Walter M. "Networks and viral evolution." Journal of Molecular Evolution 44, S1 (January 1997): S65—S75. http://dx.doi.org/10.1007/pl00000059.

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29

Hellmann, Tim, and Mathias Staudigl. "Evolution of social networks." European Journal of Operational Research 234, no. 3 (May 2014): 583–96. http://dx.doi.org/10.1016/j.ejor.2013.08.022.

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30

Lagasse, Paul, Piet Demeester, Ann Ackaert, Bart Van Caenegem, Wim Van Parys, Michael J. O'Mahony, Kristian Stubkjaer, and Jacques Benoit. "Evolution Towards Photonic Networks." European Transactions on Telecommunications 10, no. 6 (November 1999): 637–45. http://dx.doi.org/10.1002/ett.4460100608.

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31

Schuster, Peter, and Peter F. Stadler. "Networks in molecular evolution." Complexity 8, no. 1 (September 2002): 34–42. http://dx.doi.org/10.1002/cplx.10052.

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32

STROZZI, F., K. POLJANSEK, F. BONO, E. GUTIÉRREZ, and J. M. ZALDÍVAR. "RECURRENCE NETWORKS: EVOLUTION AND ROBUSTNESS." International Journal of Bifurcation and Chaos 21, no. 04 (April 2011): 1047–63. http://dx.doi.org/10.1142/s0218127411028891.

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We analyze networks generated by the recurrence plots of the time series of chaotic systems and study their properties, evolution and robustness against several types of attacks. Evolving recurrence networks obtained from chaotic systems display interesting features from the point of view of robustness (in particular, those related to their connectivity), which could help in the design of systems with high capability and robustness for information diffusion. The approach is extended to cases where the equations of the chaotic system are not given (but are defined by their time series) using state-space reconstruction methods and we note that the general characteristics of the attractors generated by such systems are maintained under this transformation. A comparison with well-known complex network models is performed to illustrate the differences and similarities.

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33

Zhang, Lei, Jianxiang Cao, and Jianyu Li. "Complex Networks: Statistical Properties, Community Structure, and Evolution." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/590794.

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We investigate the function for different networks based on complex network theory. In this paper, we choose five data sets from various areas to study. In the study of Chinese network, scale-free effect and hierarchical structure features are found in this complex system. These results indicate that the discovered features of Chinese character structure reflect the combination nature of Chinese characters. In addition, we study the community structure in Chinese character network. We can find that community structure is always considered as one of the most significant features in complex networks, and it plays an important role in the topology and function of the networks. Furthermore, we cut all the nodes in the different networks from low degree to high degree and then obtain many networks with different scale. According to the study, two interesting results have been obtained. First, the relationship between the node number of the maximum communities and the number of communities in the corresponding networks is studied and it is linear. Second, when the number of nodes in the maximum communities is increasing, the increasing tendency of the number of its edges slows down; we predict the complex networks have sparsity. The study effectively explains the characteristic and community structure evolution on different networks.

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34

KLEIN, LEANDER LUIZ, and BRENO AUGUSTO DINIZ PEREIRA. "Interdependence between networks and member firms in the evolution of inter-organizational networks." Cadernos EBAPE.BR 17, spe (November 2019): 732–49. http://dx.doi.org/10.1590/1679-395174636x.

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Abstract Cooperation among firms through networks is a form to obtain a variety of resources. Over time, networks can become an exclusive provider of some resources used by members, leading to dependencys. This study is interested in this phenomenon, and aims to examine how firm-network interdependence grows throughout the development of inter-organizational networks. The research was conducted with networks that were in distinct stages of evolution (formation, development, and professionalization). Interviews were carried out with the presidents of the networks and two member firms of each network. The study identified an inversion in the relation of interdependence investigated, where the network is dependent of its members in the first stages of evolution and, as its governance and structure consolidate, members develop a dependency relationship toward the network and the benefits it offers.

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35

Yang, Jin-Xuan. "Epidemic spreading on evolving networks." International Journal of Modern Physics B 33, no. 23 (September 20, 2019): 1950266. http://dx.doi.org/10.1142/s0217979219502667.

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Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

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36

Kaçar, Betül, and Eric A. Gaucher. "Experimental evolution of protein–protein interaction networks." Biochemical Journal 453, no. 3 (July 12, 2013): 311–19. http://dx.doi.org/10.1042/bj20130205.

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The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.

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37

Podroiko, Ye V., and Yu M. Lysetskyi. "Network technologies: evolution and peculiarities. Mathematical machines and systems." Mathematical machines and systems 2 (2020): 14–29. http://dx.doi.org/10.34121/1028-9763-2020-2-14-29.

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Today corporate network is seen as a complex system and traditionally provides the set of interacting essential components, such as: Main Site – a network of head office; Remote Site (Branch) – networks of remote office; WAN – global network uniting networks of the offices; LAN – a local network; WAN Edge – a point of connection to WAN. Internet Edge – a point of connection to the Internet; Data Cen-ter – corporate centre of data processing. Some sources also regard Service Block as a component, which is a separate segment of the network with specific services. Every component of corporate network fea-tures contains individual set of technologies, each having its history of origination and development. The paper offers short review of basic technologies which form the history of development of corporate network, as well as their evolution from a set of separated network technologies to a unified multi-service network infrastructure. This unified infrastructure is inextricably linked with a global network of Internet which is both a service and a carrier for majority of modern corporate networks. The paper de-scribes origination and development of Internet, local and global networks, Wi-Fi networks and software defined networks. Corporate network has been through a long evolution from co-existence of separated technologies to modern unified intellectual network infrastructure with high security and reliable man-agement. Due to fast-moving development of information technologies the corporate networks have dynamically transformed in several directions: network functions virtualization (NFV – Network Func-tions Virtualization); utilization of SDN solutions; automation of management processes; analytics; se-curity; cloud services. In the course of such a transformation the corporate network turned into unified, flexible, application oriented infrastructure with high reliability, easily modified and expanded function-ality, single management center, unified security policies, fast and detailed analysis of internal network processes.

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Høverstad, Boye Annfelt. "Noise and the Evolution of Neural Network Modularity." Artificial Life 17, no. 1 (January 2011): 33–50. http://dx.doi.org/10.1162/artl_a_00016.

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We study the selective advantage of modularity in artificially evolved networks. Modularity abounds in complex systems in the real world. However, experimental evidence for the selective advantage of network modularity has been elusive unless it has been supported or mandated by the genetic representation. The evolutionary origin of modularity is thus still debated: whether networks are modular because of the process that created them, or the process has evolved to produce modular networks. It is commonly argued that network modularity is beneficial under noisy conditions, but experimental support for this is still very limited. In this article, we evolve nonlinear artificial neural network classifiers for a binary classification task with a modular structure. When noise is added to the edge weights of the networks, modular network topologies evolve, even without representational support.

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Karsdorp, Folgert, and Antal van den Bosch. "The structure and evolution of story networks." Royal Society Open Science 3, no. 6 (June 2016): 160071. http://dx.doi.org/10.1098/rsos.160071.

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With this study, we advance the understanding about the processes through which stories are retold. A collection of story retellings can be considered as a network of stories, in which links between stories represent pre-textual (or ancestral) relationships. This study provides a mechanistic understanding of the structure and evolution of such story networks: we construct a story network for a large diachronic collection of Dutch literary retellings of Red Riding Hood , and compare this network to one derived from a corpus of paper chain letters. In the analysis, we first provide empirical evidence that the formation of these story networks is subject to age-dependent selection processes with a strong lopsidedness towards shorter time-spans between stories and their pre-texts (i.e. ‘young’ story versions are preferred in producing new versions). Subsequently, we systematically compare these findings with and among predictions of various formal models of network growth to determine more precisely which kinds of attractiveness are also at play or might even be preferred as explicatory models. By carefully studying the structure and evolution of the two story networks, then, we show that existing stories are differentially preferred to function as a new version's pre-text given three types of attractiveness: (i) frequency-based and (ii) model-based attractiveness which (iii) decays in time.

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40

Chen, Xi, Lan Zhang, and Wei Li. "A Network Evolution Model for Chinese Traditional Acquaintance Networks." IEEE Intelligent Systems 29, no. 5 (September 2014): 5–13. http://dx.doi.org/10.1109/mis.2013.125.

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41

Ye, Ye, Xiao Rong Hang, Jin Ming Koh, Jarosław Adam Miszczak, Kang Hao Cheong, and Neng-gang Xie. "Passive network evolution promotes group welfare in complex networks." Chaos, Solitons & Fractals 130 (January 2020): 109464. http://dx.doi.org/10.1016/j.chaos.2019.109464.

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42

Babu, M. Madan. "Structure, evolution and dynamics of transcriptional regulatory networks." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1155–78. http://dx.doi.org/10.1042/bst0381155.

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The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

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43

Wang, Gang, Yu Li Lei, Chong Jun Wang, and Shao Jie Qiao. "Community Evolution in Dynamic Social Networks." Advanced Materials Research 756-759 (September 2013): 2634–38. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2634.

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This paper proposed a framework and an algorithm for identifying communities in dynamic social networks. In order to handle the drawbacks of traditional approaches for social network analysis, we utilize the community similarities and infrequent change of community members combined with community structure optimization to develop a Group-based social community identification model to analyze the change of social interaction network with multiple time steps. According to this model ,we introduced a greed-cut algorithm and depth-search-first approach and combine them to develop a new algorithm for dynamic social interaction network recognition (called ADSIN). In addition, we conduct experiments on the dataset of Southern Women, the experiment results validate the accuracy and effectiveness of ADSIN.

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44

Li, Lei, Kun Qin, and Desheng Wu. "A hybrid Approach for the Assessment of Risk Spillover to ESG Investment in Financial Networks." Sustainability 15, no. 7 (April 2, 2023): 6123. http://dx.doi.org/10.3390/su15076123.

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In this paper, we present a framework for evaluating risk contagion by merging financial networks with machine learning techniques. The framework begins with building a financial network model based on the inter-institutional correlation network, followed by analyzing the structure and overall value changes of the financial network under the stress of a liquidation shock. We then examine the network’s evolution over time. We also use three machine learning techniques to assess the abnormal volatility of important financial institutions in the financial network. Finally, we evaluate the spillover effects of risk volatility in financial networks on ESG investments. The findings suggest that the financial network becomes more robust as the connections among financial institutions become more intricate. This leads to an improvement in the ability of the financial network to withstand systemic risk events. Overall, our study provides evidence of the negative impact of risk spillovers in financial networks on ESG investments, highlighting the need for a more sustainable and resilient financial system. This innovative framework combining financial network modeling and machine learning prediction provides a deeper understanding of the evolution of financial networks and a more accurate evaluation of abnormal volatility in financial networks.

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45

Schaden, Gerhard. "The evolution of Lexical Usage Profiles in social networks." Computational Construction Grammar and Constructional Change 30 (December 19, 2016): 193–217. http://dx.doi.org/10.1075/bjl.30.09sch.

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This paper investigates how network structure influences the outcomes of reinforcement learning in a series of multi-agent simulations. Its basic results are the following: (i) contact between agents in networks creates similarity in the usage patterns of the signals these agents use; (ii) in case of complete networks, the bigger the network, the smaller the lexical differentiation; and (iii) in networks consisting of linked cliques, the distance between usage patterns reflects on average the structure of the network.

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46

Sun, Rui. "Complex Network Evolution Model Based on Node Attraction." Applied Mechanics and Materials 596 (July 2014): 843–46. http://dx.doi.org/10.4028/www.scientific.net/amm.596.843.

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This paper studied the evolution law of the real-world networks, and then proposed a complex network model based on node attraction with tunable parameters in order to solve the problems existing in BA model and the original node attraction model. The model considered the effects of preferential attachments by the changes of degree and node attraction in the evolution process of networks. Theory research and simulation analysis show that we can more flexible adjust the evolution process of network through adjusting model parameters, therefore make it more accord with the network topology and statistical characteristics of real-world networks.

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47

Arenius, Pia, and Katja Laitinen. "Entrepreneurial Teams and the Evolution of Networks: A Longitudinal Study." International Journal of Entrepreneurship and Innovation 12, no. 4 (November 2011): 239–47. http://dx.doi.org/10.5367/ijei.2011.0054.

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To study the evolution of entrepreneurial networks, the authors followed a single firm for three years and collected data on changes in the discussion networks of the entrepreneurs. Whereas previous research has demonstrated the heterogeneity of networks between firms, this paper shows how entrepreneurs inside an organization differ in terms of networks and network resources. The authors combine the network data with qualitative interview data in an attempt to explain the observed individual-level differences. On the basis of the empirical material, they present a series of propositions linking individuals, entrepreneurial teams and network evolution.

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Paranamana, Pushpi, Pei Wang, and Patrick Shafto. "Evolution of beliefs in social networks." Collective Intelligence 1, no. 2 (October 2022): 263391372211111. http://dx.doi.org/10.1177/26339137221111151.

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Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have studied both horizontal and vertical transmission separately. Extending prior work, we propose a new theoretical framework which allows application of tools from Markov chain theory to the analysis of belief evolution via horizontal and vertical transmission. We analyze three cases: static network, randomly changing network, and homophily-based dynamic network. Whereas the former two assume network structure is independent of beliefs, the latter assumes that people tend to communicate with those who have similar beliefs. We prove under general conditions that both static and randomly changing networks converge to a single set of beliefs among all individuals along with the rate of convergence. We prove that homophily-based network structures do not in general converge to a single set of beliefs shared by all and prove lower bounds on the number of different limiting beliefs as a function of initial beliefs. We conclude by discussing implications for prior theories and directions for future work.

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49

Wang, Jiwu, and Hongbo Sun. "An evolution simulation framework for ecological structure of crowd networks." International Journal of Crowd Science 4, no. 1 (December 16, 2019): 87–100. http://dx.doi.org/10.1108/ijcs-09-2019-0022.

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Purpose This paper aims to obtain optimal specialization mode and level for complex network or system structures. In the e-commerce system, this paper studies the changes of each transaction subject in the process of ecological structure based on the income level of each transaction subject. Design/methodology/approach This paper aims to research the change of transaction efficiency evolution process of intermediaries. With the improvement of transaction efficiency, intermediaries interact with other transaction subjects at given modes in e-commerce systems. This paper analyzes the relationship between the factors of production and trade and explains the quantitative relationship between them in the form of mathematical modeling. An evolution simulation framework is established to elaborate the simulation process and method of crowd network in e-commerce ecosystem and then sets up the simulation experiment. Findings During simulation processes, the changes of data are observed and analyzed to obtain the optimal evolution paths and specialization modes. Furthermore, this paper provides solid supports for the research of the quantitative analysis of ecological structure evolutions. Originality/value Evolution simulation of ecological structure is first proposed in the topic of crowd network. It is with the aid of the concept of ecology, the theory and method, simulation of complex network structure and system structure. This paper analyses and researches the evolution process of optimal specialization modes and intelligent level of crowd networks with transaction efficiency changing. The ecological structure optimal evolution paths can be obtained by trend of simulations.

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Qazvinian, Vahed, and Dragomir R. Radev. "The Evolution of Scientific Paper Title Networks." Proceedings of the International AAAI Conference on Web and Social Media 3, no. 1 (March 20, 2009): 296–99. http://dx.doi.org/10.1609/icwsm.v3i1.13999.

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In spite of enormous previous efforts to model the growth of various networks, there have only been a few works that successfully describe the evolution of latent networks. In a latent network edges do not represent interactions between nodes, but show some proximity values. In this paper we analyze the structure and evolution of a specific type of latent networks over time by looking at a wide range of document similarity networks, in which scientific titles are nodes and their similarities are weighted edges. We use scientific papers as the corpora in order to determine the behavior of authors in choosing words for article titles. The aim of our work is to see whether term selection for titles depends on earlier published titles.

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