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Journal articles on the topic 'Complex network'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Guillaume, Jean-Loup, and Matthieu Latapy. "Complex Network Metrology." Complex Systems 16, no. 1 (2005): 83–94. http://dx.doi.org/10.25088/complexsystems.16.1.83.

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In order to study complex networks like the Internet, the World Wide Web, social networks, or biological networks, one first has to explore them. This gives a partial and biased view of the real object, which is generally assumed to be representative of the whole. However, until now nobody knows how and how much the measure influences the results. Using the example of the Internet and a rough model of its exploration process, we show that the way a given complex network is explored may strongly influence the observed properties. This leads us to argue for the necessity of developing a science

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2

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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3

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

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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, how

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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 mem

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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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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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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 w

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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 gr

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10

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 an

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11

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

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Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network

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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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13

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 n

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Chao, Wang*, Shen Anwei, and Guo Jilian. "Research on Cascading Failure in Complex Networks Based on Generative Functions." INTERNATIONAL JOURNAL OF SOCIAL SCIENCE, INNOVATION AND EDUCATIONAL TECHNOLOGIES (ONLINE) - ISSN: 2717-7130 1, no. 3 (2020): 229–36. https://doi.org/10.5281/zenodo.3975114.

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Interdependent networks have become a hot research direction in the field of complex networks in recent years. In order to study the robustness of interdependent networks, firstly, a generation function was introduced as a tool for complex network calculations. A single complex network generation function was analyzed, combined with the interdependent network modeling mechanism. The theory of robustness of interdependent networks based on generative functions is presented. The results show that as the removal ratio p increases, the remaining giant component P is a function of the removal rati

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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 th

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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 wirel

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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

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18

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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19

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

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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

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21

Goryashko, Alexander, and Pavel Bocharov. "Complex Network Formation as Antagonistic Game: Numerical Modeling." International Journal of Media and Networks 2, no. 12 (2024): 01–07. https://doi.org/10.33140/ijmn.02.12.01.

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The basic challenges of this work are twofold: demonstrating the dependence between the functional and topological qualities of partition networks and finding the simplest—with respect to algorithmic complexity—network elements. The study of these problems is based on finding the solution to an appropriate antagonistic vertex game. The results of the numerical simulations of antagonistic partition games demonstrate that the winner’s graphs are “almost always” dense and hyperenergetic compared to the loser’s graphs. These observations reveal that successful evolutionary mechanisms can be realiz

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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-distort

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23

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

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24

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

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If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportati

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

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

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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 collabo

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LEYVA, I., I. SENDIÑA-NADAL, J. A. ALMENDRAL, et al. "ENTRAINMENT COMPETITION IN COMPLEX NETWORKS." International Journal of Bifurcation and Chaos 20, no. 03 (2010): 827–33. http://dx.doi.org/10.1142/s0218127410026113.

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

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

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Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of

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

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

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31

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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32

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

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Epilepsy is a disease of abnormal neural activities involving large area of brain networks. Until now the nature of functional brain network associated with epilepsy is still unclear. Recent researches indicate that the small world or scale-free attributes and the occurrence of highly clustered connection patterns could represent a general organizational principle in the human brain functional network. In this paper, we seek to find whether the small world or scale-free property of brain network is correlated with epilepsy seizure formation. A mass neural model was adopted to generate multiple

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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 i

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

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social c

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

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In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social c

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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 In

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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 netw

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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

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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

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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

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SHAO, CHEN-XI, HUI-LING DOU, and BING-HONG WANG. "INFORMATION ASYMMETRY FLOWING IN COMPLEX NETWORKS." International Journal of Modern Physics B 26, no. 31 (2012): 1250183. http://dx.doi.org/10.1142/s0217979212501834.

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The concept of information asymmetry in complex networks is introduced on the basis of information asymmetry in economics and symmetry breaking. Information flowing between two nodes on a link is bidirectional, whose size is closely related to traffic dynamics on the network. Based on asymmetric information theory, we proposed information flow between network nodes is asymmetrical. We designed two methods to calculate the amount of information flow based on two mechanisms of complex network. Unequal flow of two opposite directions on the same link proved information asymmetry exists in the com

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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 comp

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

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

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Ratnayake, Prasan, Sugandima Weragoda, Janaka Wansapura, Dharshana Kasthurirathna, and Mahendra Piraveenan. "Quantifying the Robustness of Complex Networks with Heterogeneous Nodes." Mathematics 9, no. 21 (2021): 2769. http://dx.doi.org/10.3390/math9212769.

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The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks

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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

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Pizzuti, Clara, and Annalisa Socievole. "Computation in Complex Networks." Entropy 23, no. 2 (2021): 192. http://dx.doi.org/10.3390/e23020192.

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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 re

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