Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been...Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.展开更多
Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate ...Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.展开更多
Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps....Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.展开更多
In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in...In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in network to reveal the inner principle of complex network with the feature of small world in aspect of adjacent matrix and community structure,aviation network adjacent matrix of China was transformed according to the node rank and the matrix was arranged on the basis of ascending node rank with the center point as original point.Adjacent probability from the original point to extension around in approximate area was calculated.Through fitting probability distribution curve,power function of probability distribution of edge in adjacent matrix arranged by ascending node rank was found.According to the feature of adjacent probability distribution,deleting step by step with node rank ascending algorithm was set up to search non-overlap community structure in network and the flow chart of algorithm was given.A non-overlap community structure with 10 different scale communities in aviation network of China was found by the computer program written on the basis of this algorithm.展开更多
The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for per...The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.展开更多
In this paper,we propose a simple model of opinion dynamics to construct social networks,based on the algorithm of link rewiring of local attachment(RLA)and global attachment(RGA).Generality,the system does reach a st...In this paper,we propose a simple model of opinion dynamics to construct social networks,based on the algorithm of link rewiring of local attachment(RLA)and global attachment(RGA).Generality,the system does reach a steady state where all individuals'opinion and the complex network structure are fixed.The RGA enhances the ability of consensus of opinion formation.Furthermore,by tuning a model parameter p,which governs the proportion of RLA and RGA,we find the formation of hierarchical structure in the social networks for p>p_(c).Here,p_(c) is related to the complex network size N and the minimal coordination number 2K.The model also reproduces many features of large social networks,including the“weak links”property.展开更多
There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of...There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.展开更多
The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior...The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.展开更多
We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It cha...We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.展开更多
Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the pos...This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the possible maximum estimate of the domain of structural exponential stability are determined. The filtering ability of the associative neural networks contaminated by input noises is analyzed. Employing the obtained results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical associative neural network that stores a given set of vectors as the stable equilibrium points as well as learns new patterns can be developed. Some new concepts defined here are expected to be the instruction for further studies of learning associative neural networks.展开更多
The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalanc...The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalance is an important indicator to measure the network tension.This paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm.Experiments on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed method.In EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.展开更多
Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularl...Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularly crucial,as it assists in uncovering hidden patterns within the network.We construct a seed node selection method based on the local structural characteristics of network nodes and,by integrating deep learning methods,establish a local information expansion strategy.This approach effectively identifies and expands community boundaries,developing a novel multilayer network community detection algorithm—the Layered Information Expansion Detection Algorithm(LIEDA).Its exceptional performance has been experimentally verified using multiple real-world datasets.Compared with existing technologies,the LIEDA has considerable accuracy,stability,and adaptability advantages.Compared with various popular benchmark algorithms,the model has substantially improved multiple evaluation metrics across several authoritative public and synthetic datasets.展开更多
We study the epidemic spreading of the susceptible-infected-susceptible model on small-world networks with modular structure. It is found that the epidemic threshold increases linearly with the modular strength. Furth...We study the epidemic spreading of the susceptible-infected-susceptible model on small-world networks with modular structure. It is found that the epidemic threshold increases linearly with the modular strength. Furthermore, the modular structure may influence the infected density in the steady state and the spreading velocity at the beginning of propagation. Practically, the propagation can be hindered by strengthening the modular structure in the view of network topology. In addition, to reduce the probability of reconnection between modules may also help to control the propagation.展开更多
Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling many complex natural and artificial systems. Oil-water two-phase flow is one of t...Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling many complex natural and artificial systems. Oil-water two-phase flow is one of the most complex systems. In this paper, we use complex networks to study the inclined oil water two-phase flow. Two different complex network construction methods are proposed to build two types of networks, i.e. the flow pattern complex network （FPCN） and fluid dynamic complex network （FDCN）. Through detecting the community structure of FPCN by the community-detection algorithm based on K-means clustering, useful and interesting results are found which can be used for identifying three inclined oil-water flow patterns. To investigate the dynamic characteristics of the inclined oil-water two-phase flow, we construct 48 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of the inclined oil-water two-phase flow. In this paper, from a new perspective, we not only introduce a complex network theory into the study of the oil-water two-phase flow but also indicate that the complex network may be a powerful tool for exploring nonlinear time series in practice.展开更多
In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly f...In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly from topological information. This paper shows, by a simple example where scientists in three research groups and one external group form four communities, that in some real world networks non-topological information （in this example, the research group affiliation） dominates community division. If the information has some influence on the network topological structure, the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology. We show that weighted Newman algorithm may be the best choice for this example. We believe that this idea is general for real-world complex networks.展开更多
基金This work is supported by the NSFC Major Research Program under Grant No. 60496321, the National Natural Science Foundation of China under Grant No. 60503016, and the National High-Tech Development 863 Program of China under Grant No. 2003AA118020..
文摘Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
基金supported by the Natural Science Foundation of China(No.U22A2099)the Innovation Project of Guangxi Graduate Education(YCBZ2023130).
文摘Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific category at each updated timestamp. Then, DSNGA-CSP achieves intra-community and inter-community anonymization separately to retain more of the community structure of the original graph at each timestamp. It anonymizes community subgraphs by the proposed novel -composition method and anonymizes inter-community edges by edge isomorphism. Finally, a novel information loss metric is introduced in DSNGA-CSP to precisely capture the utility of the anonymized graph through original information preservation and anonymous information changes. Extensive experiments conducted on five real-world datasets demonstrate that DSNGA-CSP consistently outperforms existing methods, providing a more effective balance between privacy and utility. Specifically, DSNGA-CSP shows an average utility improvement of approximately 30% compared to TAKG and CTKGA for three dynamic graph datasets, according to the proposed information loss metric IL.
基金Supported by the National Natural Science Foundation of China(61374180,61373136,61304169)the Research Foundation for Humanities and Social Sciences of Ministry of Education,China(12YJAZH120)+1 种基金the Six Projects Sponsoring Talent Summits of Jiangsu Province,China(RLD201212)the Natural Science Foundation of Anhui Province(1608085MF127)
文摘Considering the actual behavior of people’s short-term travel,we propose a dynamic small-world community network model with tunable community strength which has constant local links and time varying long-range jumps.Then an epidemic model of susceptible-infected-recovered is established based on the mean-field method to evaluate the inhibitory effects of avoidance and immunization on epidemic spreading.And an approximate formula for the epidemic threshold is obtained by mathematical analysis.The simulation results show that the epidemic threshold decreases with the increase of inner-community motivation rate and inter-community long-range motivation rate,while it increases with the increase of immunization rate or avoidance rate.It indicates that the inhibitory effect on epidemic spreading of immunization works better than that of avoidance.
基金National Natural Science Foundation of China(71971017).
文摘In order to discover the probability distribution feature of edge in aviation network adjacent matrix of China and on the basis of this feature to establish an algorithm of searching non-overlap community structure in network to reveal the inner principle of complex network with the feature of small world in aspect of adjacent matrix and community structure,aviation network adjacent matrix of China was transformed according to the node rank and the matrix was arranged on the basis of ascending node rank with the center point as original point.Adjacent probability from the original point to extension around in approximate area was calculated.Through fitting probability distribution curve,power function of probability distribution of edge in adjacent matrix arranged by ascending node rank was found.According to the feature of adjacent probability distribution,deleting step by step with node rank ascending algorithm was set up to search non-overlap community structure in network and the flow chart of algorithm was given.A non-overlap community structure with 10 different scale communities in aviation network of China was found by the computer program written on the basis of this algorithm.
基金National Natural Science Foundation of China(61772196,61472136)Hunan Provincial Focus Natural Science Fund(2020JJ4249)+4 种基金Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005)Postgraduate Scientific Research Innovation Project of Hunan Province(CX20201074)Hunan Technology and Business University’s 2019 school-level degree and postgraduate education and teaching reform project(YJG2019YB13)The 2020 school-level teaching reform project of Hunan Technology and Business University(School Teaching Word[2020]No.15)Research Project of Degree and Postgraduate Education Reform in Hunan Province(2020JGYB234).
文摘The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application value.However,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis methods.Based on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network.The simulation experiment results show that the calculation method in this paper can get the optimal solution faster.It provides an idea for the study of real and complex social networks.
基金supported by the National Natural Science Foundation of China(Grant Nos.10647125,10635020,10975057 and 10975062)the Programme of Introducing Talents of Discipline to Universities under Grant No.B08033.
文摘In this paper,we propose a simple model of opinion dynamics to construct social networks,based on the algorithm of link rewiring of local attachment(RLA)and global attachment(RGA).Generality,the system does reach a steady state where all individuals'opinion and the complex network structure are fixed.The RGA enhances the ability of consensus of opinion formation.Furthermore,by tuning a model parameter p,which governs the proportion of RLA and RGA,we find the formation of hierarchical structure in the social networks for p>p_(c).Here,p_(c) is related to the complex network size N and the minimal coordination number 2K.The model also reproduces many features of large social networks,including the“weak links”property.
文摘There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.
基金supported by the National Natural Science Foundation of China(No.22373024,22463006,and 52463015)the joint fund between the Gansu Provincial Science and Technology Plan Project(Natural Science Foundation)(No.23JRRA794)the Open Research Fund of the Songshan Lake Materials Laboratory(No.2023SLABFK11)。
文摘The performance of polymer networks is directly determined by their structure.Understanding the network structure offers insights into optimizing material performance,such as elasticity,toughness,and swelling behavior.Herein,in this study we introduce the Dijkstra algorithm from graph theory to characterize polymer networks based on star-shaped multi-armed precursors by employing coarse-grained molecular dynamics simulations coupled with stochastic reaction model.Our research focuses on the structure characteristics of the generated networks,including the number and size of loops,as well as network dispersity characterized by loops.Tracking the number of loops during network generation allows for the identification of the gel point.The size distribution of loops in the network is primarily related to the functionality of the precursors,and the system with fewer precursor arms exhibiting larger average loop sizes.Strain-stress curves indicate that materials with identical functionality and precursor arm lengths generally exhibit superior performance.This method of characterizing network structures helps to refine microscopic structural analysis and contributes to the enhancement and optimization of material properties.
基金Project supported by the Ministry of Education of China in the later stage of philosophy and social science research(Grant No.19JHG091)the National Natural Science Foundation of China(Grant No.72061003)+1 种基金the Major Program of National Social Science Fund of China(Grant No.20&ZD155)the Guizhou Provincial Science and Technology Projects(Grant No.[2020]4Y172)。
文摘We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
文摘This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the possible maximum estimate of the domain of structural exponential stability are determined. The filtering ability of the associative neural networks contaminated by input noises is analyzed. Employing the obtained results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical associative neural network that stores a given set of vectors as the stable equilibrium points as well as learns new patterns can be developed. Some new concepts defined here are expected to be the instruction for further studies of learning associative neural networks.
基金This work was supported by the National Natural Science Foundation of China(6177219661472136)+3 种基金the Hunan Provincial Focus Social Science Fund(2016ZDB006)Hunan Provincial Social Science Achievement Review Committee results appraisal identification project(Xiang Social Assessment 2016JD05)Key Project of Hunan Provincial Social Science Achievement Review Committee(XSP 19ZD1005)The authors gratefully acknowledge the financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology(2017TP1026).
文摘The symbolic network adds the emotional information of the relationship,that is,the“+”and“-”information of the edge,which greatly enhances the modeling ability and has wide application in many fields.Weak unbalance is an important indicator to measure the network tension.This paper starts from the weak structural equilibrium theorem,and integrates the work of predecessors,and proposes the weak unbalanced algorithm EAWSB based on evolutionary algorithm.Experiments on the large symbolic networks Epinions,Slashdot and WikiElections show the effectiveness and efficiency of the proposed method.In EAWSB,this paper proposes a compression-based indirect representation method,which effectively reduces the size of the genotype space,thus making the algorithm search more complete and easier to get better solutions.
基金supported by the National Natural Science Foundation of China(Nos.62272311 and 62102262).
文摘Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularly crucial,as it assists in uncovering hidden patterns within the network.We construct a seed node selection method based on the local structural characteristics of network nodes and,by integrating deep learning methods,establish a local information expansion strategy.This approach effectively identifies and expands community boundaries,developing a novel multilayer network community detection algorithm—the Layered Information Expansion Detection Algorithm(LIEDA).Its exceptional performance has been experimentally verified using multiple real-world datasets.Compared with existing technologies,the LIEDA has considerable accuracy,stability,and adaptability advantages.Compared with various popular benchmark algorithms,the model has substantially improved multiple evaluation metrics across several authoritative public and synthetic datasets.
基金Supported by the National Natural Science Foundation of China Grant Nos 70471088 and 70631001, the National Basic Research Programme of China under Grant No 2006CB705500.
文摘We study the epidemic spreading of the susceptible-infected-susceptible model on small-world networks with modular structure. It is found that the epidemic threshold increases linearly with the modular strength. Furthermore, the modular structure may influence the infected density in the steady state and the spreading velocity at the beginning of propagation. Practically, the propagation can be hindered by strengthening the modular structure in the view of network topology. In addition, to reduce the probability of reconnection between modules may also help to control the propagation.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 50674070 and 60374041)the National High Technology Research and Development Program of China (Grant No 2007AA06Z231)
文摘Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling many complex natural and artificial systems. Oil-water two-phase flow is one of the most complex systems. In this paper, we use complex networks to study the inclined oil water two-phase flow. Two different complex network construction methods are proposed to build two types of networks, i.e. the flow pattern complex network （FPCN） and fluid dynamic complex network （FDCN）. Through detecting the community structure of FPCN by the community-detection algorithm based on K-means clustering, useful and interesting results are found which can be used for identifying three inclined oil-water flow patterns. To investigate the dynamic characteristics of the inclined oil-water two-phase flow, we construct 48 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of the inclined oil-water two-phase flow. In this paper, from a new perspective, we not only introduce a complex network theory into the study of the oil-water two-phase flow but also indicate that the complex network may be a powerful tool for exploring nonlinear time series in practice.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.70671089 and 10635040)
文摘In a network described by a graph, only topological structure information is considered to determine how the nodes are connected by edges. Non-topological information denotes that which cannot be determined directly from topological information. This paper shows, by a simple example where scientists in three research groups and one external group form four communities, that in some real world networks non-topological information （in this example, the research group affiliation） dominates community division. If the information has some influence on the network topological structure, the question arises as to how to find a suitable algorithm to identify the communities based only on the network topology. We show that weighted Newman algorithm may be the best choice for this example. We believe that this idea is general for real-world complex networks.
