Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolvi...Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.展开更多
With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality st...With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality stability.To address the challenge that existing partitioning methods are inad-equate for the planning and operation needs of active distribution networks under frequently changing power flow conditions,a three-stage dynamic partitioning approach is proposed based on an im-proved sand cat swarm optimization(ISCSO)algorithm.Firstly,a comprehensive dynamic partitio-ning index is developed by integrating both structural and functional metrics,including modularity,voltage regulation capability,and regional renewable energy accommodation capacity.Secondly,to overcome the limitations of the conventional sand cat swarm optimization,namely its weak global ex-ploration ability and tendency to fall into local optima in the later optimization stages,chaotic map-ping is employed to initialize a uniformly distributed population.A nonlinear sensitivity mechanism is introduced to balance global exploration and local exploitation,alongside the design of a particle encoding and position updating scheme tailored for dynamic partitioning.Furthermore,a‘state re-tention-local adjustment-global reconstruction’partitioning structure is developed.To avoid unnec-essary partition changes under minor source-load fluctuations,the concept of overlapping nodes is introduced,enabling fine-tuned adjustments under such conditions.Finally,two experimental sce-narios are designed to validate the proposed method.Simulation results demonstrate strong electrical coupling performance and show that the method enhances voltage regulation and renewable energy integration capabilities across regions.展开更多
There has been considerable interest in designing algorithms for detecting community structure in real-world complex networks. A majority of these algorithms assume that communities are disjoint, placing each vertex i...There has been considerable interest in designing algorithms for detecting community structure in real-world complex networks. A majority of these algorithms assume that communities are disjoint, placing each vertex in only one cluster. However, in nature, it is a matter of common experience that communities often overlap and members often play multiple roles in a network topology. To further investigate these properties of overlapping communities and heterogeneity within the network topology, a new method is proposed to divide networks into separate communities by spreading outward from each local important element and extracting its neighbors within the same group in each spreading operation. When compared with the state of the art, our new algorithm can not only classify different types of nodes at a more fine-grained scale successfully but also detect community structure more effectively. We also evaluate our algorithm using the standard data sets. Our results show that it performed well not only in the efficiency of algorithm, but also with a higher accuracy of partition results.展开更多
基金supported by the National Natural Science Foundation of China(615730176140149961174162)
文摘Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foun-dation Project(No.62303006).
文摘With the large-scale integration of renewable energy sources into the grid,distribution networks are increasingly challenged by issues related to renewable energy accommodation and the mainte-nance of power quality stability.To address the challenge that existing partitioning methods are inad-equate for the planning and operation needs of active distribution networks under frequently changing power flow conditions,a three-stage dynamic partitioning approach is proposed based on an im-proved sand cat swarm optimization(ISCSO)algorithm.Firstly,a comprehensive dynamic partitio-ning index is developed by integrating both structural and functional metrics,including modularity,voltage regulation capability,and regional renewable energy accommodation capacity.Secondly,to overcome the limitations of the conventional sand cat swarm optimization,namely its weak global ex-ploration ability and tendency to fall into local optima in the later optimization stages,chaotic map-ping is employed to initialize a uniformly distributed population.A nonlinear sensitivity mechanism is introduced to balance global exploration and local exploitation,alongside the design of a particle encoding and position updating scheme tailored for dynamic partitioning.Furthermore,a‘state re-tention-local adjustment-global reconstruction’partitioning structure is developed.To avoid unnec-essary partition changes under minor source-load fluctuations,the concept of overlapping nodes is introduced,enabling fine-tuned adjustments under such conditions.Finally,two experimental sce-narios are designed to validate the proposed method.Simulation results demonstrate strong electrical coupling performance and show that the method enhances voltage regulation and renewable energy integration capabilities across regions.
文摘There has been considerable interest in designing algorithms for detecting community structure in real-world complex networks. A majority of these algorithms assume that communities are disjoint, placing each vertex in only one cluster. However, in nature, it is a matter of common experience that communities often overlap and members often play multiple roles in a network topology. To further investigate these properties of overlapping communities and heterogeneity within the network topology, a new method is proposed to divide networks into separate communities by spreading outward from each local important element and extracting its neighbors within the same group in each spreading operation. When compared with the state of the art, our new algorithm can not only classify different types of nodes at a more fine-grained scale successfully but also detect community structure more effectively. We also evaluate our algorithm using the standard data sets. Our results show that it performed well not only in the efficiency of algorithm, but also with a higher accuracy of partition results.
