Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
In this paper, a dynamic epidemic control model on the uncorrelated complex networks is proposed. By means of theoretical analysis, we found that the new model has a similar epidemic threshold as that of the susceptib...In this paper, a dynamic epidemic control model on the uncorrelated complex networks is proposed. By means of theoretical analysis, we found that the new model has a similar epidemic threshold as that of the susceptible-infectedrecovered （SIR） model on the above networks, but it can reduce the prevalence of the infected individuals remarkably. This result may help us understand epidemic spreading phenomena on real networks and design appropriate strategies to control infections.展开更多
The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchic...The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.展开更多
The paper presents a novel hydraulic fracturing model for the characterization and simulation of the complex fracture network in shale gas reservoirs. We go beyond the existing method that uses planar or orthogonal co...The paper presents a novel hydraulic fracturing model for the characterization and simulation of the complex fracture network in shale gas reservoirs. We go beyond the existing method that uses planar or orthogonal conjugate fractures for representing the ＇＇complexity＇＇ of the network. Bifurcation of fractures is performed utilizing the Lindenmayer system based on fractal geometry to describe the fracture propagation pattern, density and network connectivity. Four controlling parameters are proposed to describe the details of complex fractures and stimulated reservoir volume（SRV）. The results show that due to the multilevel feature of fractal fractures, the model could provide a simple method for contributing reservoir volume calibration. The primary-and second-stage fracture networks across the overall SRV are the main contributions to the production, while the induced fracture network just contributes another 20% in the late producing period. We also conduct simulation with respect to different refracturing cases and find that increasing the complexity of the fracture network provides better performance than only enhancing the fracture conductivity.展开更多
In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the model...In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.展开更多
We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature dis...We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.展开更多
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node ...In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the network.In our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node i.That is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network structure.We show that depending on the aging parameter α two different network topologies can emerge.For α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/m.Moreover,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/m.For α>1,such scaling behavior disappears and the degree distribution is exponential.展开更多
The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite siz...The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS （susceptibleinfected-susceptible） model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.展开更多
In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. ...In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. By employing the complex network theory, especially its statistical indicators, the railway accident as well as its key causations can be analyzed from the overall perspective. As a case, the ＂7.23＂ China-Yongwen railway accident is illustrated based on this model. The results show that the inspection of signals and the checking of line conditions before trains run played an important role in this railway accident. In conclusion, the constructed model gives a theoretical clue for railway accident prediction and, hence, greatly reduces the occurrence of railway accidents.展开更多
We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the activ...We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.展开更多
In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of pub...In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of public opinion propagation into two layers:the original topic layer and the derived topic layer.Messages are transmitted separately by the SEIR model in the two topic layers,which are independent and interactive.The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold.Further,we establish the relationship between the original topic and the derived topic by simulation.This paper uses the Baidu index to demonstrate the correctness of the model.The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method.The results show that the proposed model is consistent with the propagation of actual public opinion.展开更多
To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model,...To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.展开更多
A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the unc...A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method（RIM）to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered（SIR） model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness,and eigenvector centrality methods.展开更多
In living cells, proteins are dynamically connec ted through biochemical reactions, so its functi onal features are properly encoded into protein protein interaction networks (PINs). Up to pres ent, many efforts have ...In living cells, proteins are dynamically connec ted through biochemical reactions, so its functi onal features are properly encoded into protein protein interaction networks (PINs). Up to pres ent, many efforts have been devoted to exploring the basic feature of PINs. However, it is still a challenging problem to explore a universal pr operty of PINs. Here we employed the complex networks theory to analyze the proteinprotein interactions from Database of Interacting Prot ein. Complex tree: the unique framework of PINs was revealed by three topological properties of the giant component of PINs (GCOP), including rightskewed degree distributions, relatively sm all clustering coefficients and short characteristic path lengths. Furthermore, we proposed a no nlinearly growth model: complex tree model to reflect the tree framework, the simulation resu lts of this model showed that GCOPs were well represented by our model, which could be help ful for understanding the treestructure: basic framework of PINs. Source code and binaries freely available for download at http://cic.scu. edu.cn/bioinformatics/STM/STM_code.rar.展开更多
On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average ...On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.展开更多
In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi...In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金Project supported by the National Natural Science Foundation of China (Grant No.60774088)the Program for New Century Excellent Talents of Higher Education of China (Grant No NCET 2005-290)the Special Research Fund for the Doctoral Program of Higher Education of China (Grant No 20050055013)
文摘In this paper, a dynamic epidemic control model on the uncorrelated complex networks is proposed. By means of theoretical analysis, we found that the new model has a similar epidemic threshold as that of the susceptible-infectedrecovered （SIR） model on the above networks, but it can reduce the prevalence of the infected individuals remarkably. This result may help us understand epidemic spreading phenomena on real networks and design appropriate strategies to control infections.
文摘The complexity of large-scale network systems made of a large number of nonlinearly interconnected components is a restrictive facet for their modeling and analysis. In this paper, we propose a framework of hierarchical modeling of a complex network system, based on a recursive unsupervised spectral clustering method. The hierarchical model serves the purpose of facilitating the management of complexity in the analysis of real-world critical infrastructures. We exemplify this by referring to the reliability analysis of the 380 kV Italian Power Transmission Network (IPTN). In this work of analysis, the classical component Importance Measures (IMs) of reliability theory have been extended to render them compatible and applicable to a complex distributed network system. By utilizing these extended IMs, the reliability properties of the IPTN system can be evaluated in the framework of the hierarchical system model, with the aim of providing risk managers with information on the risk/safety significance of system structures and components.
基金supported by National Natural Science Foundation of China(No.51674279)China Postdoctoral Science Foundation(No.2016M602227)a grant from National Science and Technology Major Project(No.2017ZX05049-006)
文摘The paper presents a novel hydraulic fracturing model for the characterization and simulation of the complex fracture network in shale gas reservoirs. We go beyond the existing method that uses planar or orthogonal conjugate fractures for representing the ＇＇complexity＇＇ of the network. Bifurcation of fractures is performed utilizing the Lindenmayer system based on fractal geometry to describe the fracture propagation pattern, density and network connectivity. Four controlling parameters are proposed to describe the details of complex fractures and stimulated reservoir volume（SRV）. The results show that due to the multilevel feature of fractal fractures, the model could provide a simple method for contributing reservoir volume calibration. The primary-and second-stage fracture networks across the overall SRV are the main contributions to the production, while the induced fracture network just contributes another 20% in the late producing period. We also conduct simulation with respect to different refracturing cases and find that increasing the complexity of the fracture network provides better performance than only enhancing the fracture conductivity.
文摘In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60672095)the Fundamental Research Funds for the Central Universities,China (Grant No. KYZ201300)the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024)
文摘We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally. several real-world networks are investigated.
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
基金funded by the National Natural Science Foundation of China(Grant No.11601294)the Research Project Supported by Shanxi Scholarship Council of China(Grant No.2021-002)+1 种基金the Shanxi Province Science Foundation(Grant No.20210302123466)the 1331 Engineering Project of Shanxi Province。
文摘In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes.The network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the network.In our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node i.That is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network structure.We show that depending on the aging parameter α two different network topologies can emerge.For α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/m.Moreover,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/m.For α>1,such scaling behavior disappears and the degree distribution is exponential.
基金Project supported by the National Nature Science Foundation of China （Grant Nos 90204004 and 90304005）.
文摘The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS （susceptibleinfected-susceptible） model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.
基金Project supported by the National High Technology Research and Development Program of China (Grant No.2011AA110502)the National Natural Science Foundation of China (Grant No.71271022)the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety,China (Grant No.RCS2012ZQ001)
文摘In this paper, a new model is constructed for the causation analysis of railway accident based on the complex network theory. In the model, the nodes are defined as various manifest or latent accident causal factors. By employing the complex network theory, especially its statistical indicators, the railway accident as well as its key causations can be analyzed from the overall perspective. As a case, the ＂7.23＂ China-Yongwen railway accident is illustrated based on this model. The results show that the inspection of signals and the checking of line conditions before trains run played an important role in this railway accident. In conclusion, the constructed model gives a theoretical clue for railway accident prediction and, hence, greatly reduces the occurrence of railway accidents.
基金Project supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20093108110004)the National Natural Science Foundation of China (Grant Nos. 10805033 and 10902065)
文摘We study the impact of age on network evolution which couples addition of new nodes and deactivation of old ones. During evolution, each node experiences two stages： active and inactive. The transition from the active state to the inactive one is based on the rank of the node. In this paper, we adopt age as a criterion of ranking, and propose two deactivation models that generalize previous research. In model A, the older active node possesses the higher rank, whereas in model B, the younger active node takes the higher rank. We make a comparative study between the two models through the node-degree distribution.
基金in part by the National Natural Science Foundation of China(No.51334003).
文摘In view of the fact that news can generate derivative topics when it spreads through micro-blogs,a two-layer coupled SEIR public opinion propagation model is proposed in this paper.The model divides the process of public opinion propagation into two layers:the original topic layer and the derived topic layer.Messages are transmitted separately by the SEIR model in the two topic layers,which are independent and interactive.The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold.Further,we establish the relationship between the original topic and the derived topic by simulation.This paper uses the Baidu index to demonstrate the correctness of the model.The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method.The results show that the proposed model is consistent with the propagation of actual public opinion.
基金supported by National Science and Technology Major Project under Grant No. 2012ZX03004001the National Natural Science Foundation of China under Grant No. 60971083
文摘To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61374180 and 61373136)the Ministry of Education Research in the Humanities and Social Sciences Planning Fund Project,China(Grant No.12YJAZH120)the Six Projects Sponsoring Talent Summits of Jiangsu Province,China(Grant No.RLD201212)
文摘A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method（RIM）to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered（SIR） model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness,and eigenvector centrality methods.
文摘In living cells, proteins are dynamically connec ted through biochemical reactions, so its functi onal features are properly encoded into protein protein interaction networks (PINs). Up to pres ent, many efforts have been devoted to exploring the basic feature of PINs. However, it is still a challenging problem to explore a universal pr operty of PINs. Here we employed the complex networks theory to analyze the proteinprotein interactions from Database of Interacting Prot ein. Complex tree: the unique framework of PINs was revealed by three topological properties of the giant component of PINs (GCOP), including rightskewed degree distributions, relatively sm all clustering coefficients and short characteristic path lengths. Furthermore, we proposed a no nlinearly growth model: complex tree model to reflect the tree framework, the simulation resu lts of this model showed that GCOPs were well represented by our model, which could be help ful for understanding the treestructure: basic framework of PINs. Source code and binaries freely available for download at http://cic.scu. edu.cn/bioinformatics/STM/STM_code.rar.
基金Supported by the National High Technology Research and Development Program of China(No.2008AA01A201)the National Natural Science Foundation of China(No.60503015)
文摘On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.
基金supported by the Water Conservancy Science and Technology Project of Jiangsu Province(Grant No.2012041)the Jiangsu Province Ordinary University Graduate Student Research Innovation Project(Grant No.CXZZ13_0256)
文摘In this study, we simulated water flow in a water conservancy project consisting of various hydraulic structures, such as sluices, pumping stations, hydropower stations, ship locks, and culverts, and developed a multi-period and multi-variable joint optimization scheduling model for flood control, drainage, and irrigation. In this model, the number of sluice holes, pump units, and hydropower station units to be opened were used as decision variables, and different optimization objectives and constraints were considered. This model was solved with improved genetic algorithms and verified using the Huaian Water Conservancy Project as an example. The results show that the use of the joint optimization scheduling led to a 10% increase in the power generation capacity and a 15% reduction in the total energy consumption. The change in the water level was reduced by 0.25 m upstream of the Yundong Sluice, and by 50% downstream of pumping stations No. 1, No. 2, and No. 4. It is clear that the joint optimization scheduling proposed in this study can effectively improve power generation capacity of the project, minimize operating costs and energy consumption, and enable more stable operation of various hydraulic structures. The results may provide references for the management of water conservancy projects in complex river networks.
