As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is be...As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.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
Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability ...Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.展开更多
The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be diff...The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.展开更多
Abstract Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic...Abstract Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency arc established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China＇s airport networks show that the evaluation method proposed in this papcr is the most accuratc. Thc vulucrability of US and China＇s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.展开更多
In order to reduce the accident rate of consumer-grade unmanned aerial vehicles(UAVs)in daily use scenarios,the accident causes are analyzed based on the accident cases of consumer-grade UAVs.By extracting accident ca...In order to reduce the accident rate of consumer-grade unmanned aerial vehicles(UAVs)in daily use scenarios,the accident causes are analyzed based on the accident cases of consumer-grade UAVs.By extracting accident causing factors based on the Grounded theory,the relationship between these factors is analyzed.The Bayesian network for consumer-grade UAV accidents is constructed.With the Grounded theory-Bayesian network,the probability of four types of accidents is inferred:fall,air collision,disappearance,and personal injury.With the posterior probability of each factor being reversely reasoned,the causal chain with the maximum probability of each accident is obtained.After the sensitivity of each factor is analyzed,the key nodes in the network accordingly are inferred.Then the causing factors of consumer-grade UAV accidents are analyzed.The results show that the probability of fall accident is the highest,the fall accident is associated with the probabilistic maximum causal chain of personal injury,and the sensitivity analysis results of each type of accident as the result node are inconsistent.展开更多
The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu co...The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.展开更多
Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the ...Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.展开更多
Key establishment and its management in Wireless Sensor Networks(WSN) is a challenging problem due to its limited resources and disordered structure. Many key management schemes have been developed recently for WSN to...Key establishment and its management in Wireless Sensor Networks(WSN) is a challenging problem due to its limited resources and disordered structure. Many key management schemes have been developed recently for WSN to provide secure communication between source and destination sensor nodes.A serious threat highlighted in all of these schemes is that of node capture attacks,where an adversary gains full control over a sensor node through direct physical access.Node capture attacks can also be helpful to an adversary in new attacks like Blackhole and Denial of Service.All of these proposed key management solutions still suffer from node capture attacks with resilience.The current sensor networks are assumed to be designed for specific applications,having key management protocols strongly coupled to applications.The future sensor networks are envisioned as comprising heterogeneous devices assisting to a large range of applications.To achieve this goal,a new application independent approach is needed. In this paper we therefore present a novel,extended version of our previously published Dynamic Group-based Key Establishment(DGKE) protocol. We compare the proposed scheme with existing key management schemes,which shows a significant improvement in resilience against node capture attacks,memory overhead and connectivity.展开更多
To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spr...To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.展开更多
In this letter, a Function node-based Multiple Pairwise Keys Management (MPKMF) protocol for Wireless Sensor Networks (WSNs) is firstly designed, in which ordinary nodes and cluster head nodes are responsible for data...In this letter, a Function node-based Multiple Pairwise Keys Management (MPKMF) protocol for Wireless Sensor Networks (WSNs) is firstly designed, in which ordinary nodes and cluster head nodes are responsible for data collection and transmission, and function nodes are responsible for key management. There are more than one function nodes in the cluster consulting the key generation and other security decision-making. The function nodes are the second-class security center because of the characteristics of the distributed WSNs. Secondly, It is also described that the formation of function nodes and cluster heads under the control of the former, and five kinds of keys, i.e., individual key, pairwise keys, cluster key, management key, and group key. Finally, performance analysis and experiments show that, the protocol is superior in communication and energy consumption. The delay of establishing the cluster key meets the requirements, and a multiple pairwise key which adopts the coordinated security authentication scheme is provided.展开更多
Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine...Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine the key spreaders in complex networks.The existing algorithms based on gravity model consider the shortest distance between nodes.However,nodes not only influence each other through the shortest path,which will lead the loss of information between nodes.Different from the existing gravity model,the distance considered in this model is not the shortest distance between nodes,but the reciprocal of the number of feasible paths between nodes.Conveniently,the model is called FPLGM(Local Gravity Model Based on Feasible Paths).Ten different networks are utilised to verify the effectiveness of FPLGM.Results show that the FPLGM performs best in comparison with the well-known state-of-the-art methods.展开更多
Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship bet...Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.展开更多
Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is...Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is defined firstly.Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution,which is a swarm intelligence algorithm and is improved by the prior information of networks.To verify the effect of improved discrete fireworks algorithm(IDFA),experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms,and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks.The key node sets found by IDFA contain a large number of non-central nodes,which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.展开更多
基金Scientific Research Project of Liaoning Province Education Department,Code:LJKQZ20222457&LJKMZ20220781Liaoning Province Nature Fund Project,Code:No.2022-MS-291.
文摘As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.
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 National Natural Science Foundation of China(Grant No.12031002)。
文摘Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.
基金supported by the National Natural Science Foundation of China(Grant No.61961019)the Youth Key Project of the Natural Science Foundation of Jiangxi Province of China(Grant No.20202ACBL212003).
文摘The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.
基金co-supported by the National Natural Science Foundation of China(No.61039001)the Scientific Research Foundation of Civil Aviation University of China(No.2014QD01S)
文摘Abstract Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency arc established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China＇s airport networks show that the evaluation method proposed in this papcr is the most accuratc. Thc vulucrability of US and China＇s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.
基金supported by the Fun⁃damental Research Funds for the Central Universities(No.3122022103).
文摘In order to reduce the accident rate of consumer-grade unmanned aerial vehicles(UAVs)in daily use scenarios,the accident causes are analyzed based on the accident cases of consumer-grade UAVs.By extracting accident causing factors based on the Grounded theory,the relationship between these factors is analyzed.The Bayesian network for consumer-grade UAV accidents is constructed.With the Grounded theory-Bayesian network,the probability of four types of accidents is inferred:fall,air collision,disappearance,and personal injury.With the posterior probability of each factor being reversely reasoned,the causal chain with the maximum probability of each accident is obtained.After the sensitivity of each factor is analyzed,the key nodes in the network accordingly are inferred.Then the causing factors of consumer-grade UAV accidents are analyzed.The results show that the probability of fall accident is the highest,the fall accident is associated with the probabilistic maximum causal chain of personal injury,and the sensitivity analysis results of each type of accident as the result node are inconsistent.
基金supported by Jiangsu Social Science Foundation Project(Grant No:20TQC005)Philosophy Social Science Research Project Fund of Jiangsu University(Grant No:2020SJA0500)+2 种基金The National Natural Science Foundation of China(GrantNo.61802155)The Innovation and Entrepreneurship Project Fund for College Students of Jiangsu Police Academy(Grant No.202110329028Y)The“qinglan Project”of Jiangsu Universities.
文摘The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.
基金Project supported by Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ23F030012)the Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant No. GK229909299001-018)。
文摘Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.
文摘Key establishment and its management in Wireless Sensor Networks(WSN) is a challenging problem due to its limited resources and disordered structure. Many key management schemes have been developed recently for WSN to provide secure communication between source and destination sensor nodes.A serious threat highlighted in all of these schemes is that of node capture attacks,where an adversary gains full control over a sensor node through direct physical access.Node capture attacks can also be helpful to an adversary in new attacks like Blackhole and Denial of Service.All of these proposed key management solutions still suffer from node capture attacks with resilience.The current sensor networks are assumed to be designed for specific applications,having key management protocols strongly coupled to applications.The future sensor networks are envisioned as comprising heterogeneous devices assisting to a large range of applications.To achieve this goal,a new application independent approach is needed. In this paper we therefore present a novel,extended version of our previously published Dynamic Group-based Key Establishment(DGKE) protocol. We compare the proposed scheme with existing key management schemes,which shows a significant improvement in resilience against node capture attacks,memory overhead and connectivity.
文摘To discover and identify the influential nodes in any complex network has been an important issue.It is a significant factor in order to control over the network.Through control on a network,any information can be spread and stopped in a short span of time.Both targets can be achieved,since network of information can be extended and as well destroyed.So,information spread and community formation have become one of the most crucial issues in the world of SNA(Social Network Analysis).In this work,the complex network of twitter social network has been formalized and results are analyzed.For this purpose,different network metrics have been utilized.Visualization of the network is provided in its original form and then filter out(different percentages)from the network to eliminate the less impacting nodes and edges for better analysis.This network is analyzed according to different centrality measures,like edge-betweenness,betweenness centrality,closeness centrality and eigenvector centrality.Influential nodes are detected and their impact is observed on the network.The communities are analyzed in terms of network coverage considering theMinimum Spanning Tree,shortest path distribution and network diameter.It is found that these are the very effective ways to find influential and central nodes from such big social networks like Facebook,Instagram,Twitter,LinkedIn,etc.
基金Supported by the National Natural Science Foundation of China (No. 60475012)
文摘In this letter, a Function node-based Multiple Pairwise Keys Management (MPKMF) protocol for Wireless Sensor Networks (WSNs) is firstly designed, in which ordinary nodes and cluster head nodes are responsible for data collection and transmission, and function nodes are responsible for key management. There are more than one function nodes in the cluster consulting the key generation and other security decision-making. The function nodes are the second-class security center because of the characteristics of the distributed WSNs. Secondly, It is also described that the formation of function nodes and cluster heads under the control of the former, and five kinds of keys, i.e., individual key, pairwise keys, cluster key, management key, and group key. Finally, performance analysis and experiments show that, the protocol is superior in communication and energy consumption. The delay of establishing the cluster key meets the requirements, and a multiple pairwise key which adopts the coordinated security authentication scheme is provided.
基金supported by National Natural Science Foundation of China[grant number 62276058,61902057,41774063]Fundamental Research Funds for the Central Universities[grant number N2217003]+1 种基金Joint Fund of Science&Technology Department of Liaoning Province and State Key Laboratory of RoboticsChina[grant number 2020-KF-12-11]。
文摘Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases.An algorithm based on local gravity model is proposed to mine the key spreaders in complex networks.The existing algorithms based on gravity model consider the shortest distance between nodes.However,nodes not only influence each other through the shortest path,which will lead the loss of information between nodes.Different from the existing gravity model,the distance considered in this model is not the shortest distance between nodes,but the reciprocal of the number of feasible paths between nodes.Conveniently,the model is called FPLGM(Local Gravity Model Based on Feasible Paths).Ten different networks are utilised to verify the effectiveness of FPLGM.Results show that the FPLGM performs best in comparison with the well-known state-of-the-art methods.
文摘Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.
基金supported by the National Natural Science Foundation of China under Grant No.61502522。
文摘Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is defined firstly.Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution,which is a swarm intelligence algorithm and is improved by the prior information of networks.To verify the effect of improved discrete fireworks algorithm(IDFA),experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms,and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks.The key node sets found by IDFA contain a large number of non-central nodes,which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.
