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
In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined...In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.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
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
Mobile ad hoc networks create additional challenges for implementing the group key establishment due to resource constraints on nodes and dynamic changes on topology. The nodes in mobile ad hoc networks are usually lo...Mobile ad hoc networks create additional challenges for implementing the group key establishment due to resource constraints on nodes and dynamic changes on topology. The nodes in mobile ad hoc networks are usually low power devices that run on battery power. As a result, the costs of the node resources should be minimized when constructing a group key agreement protocol so that the battery life could be prolonged. To achieve this goal, in this paper we propose a security efficient group key agreement protocol based on Burmester-Desmedt （BD） scheme and layer-cluster group model, referred to as LCKM-BD, which is appropriate for large mobile ad hoe networks. In the layer-cluster group model, BD scheme is employed to establish group key, which can not only meet security demands of mobile ad hoc networks but also improve executing performance. Finally, the proposed protocol LCKM-BD are compared with BD, TGDH （tree-based group Diffe-Hellman）, and GDH （group Diffie-Hellman） group key agreement protocols. The analysis results show that our protocol can significantly decrease both the computational overhead and communication costs with respect to these comparable protocols.展开更多
Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of s...Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.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
Objective To explore the role of HIV-1 tat gene variations in AIDS dementia complex （ADC） pathogenesis. Methods HIV-1 tat genes derived from peripheral spleen and central basal ganglia of an AIDS patient with ADC an...Objective To explore the role of HIV-1 tat gene variations in AIDS dementia complex （ADC） pathogenesis. Methods HIV-1 tat genes derived from peripheral spleen and central basal ganglia of an AIDS patient with ADC and an AIDS patient without ADC were cloned for sequence analysis. HIV-1 tat gene sequence alignment was performed by using CLUSTAL W and the phylogentic analysis was conducted by using Neighbor-joining with MEGA4 software. All tat genes were used to construct recombinant retroviral expressing vector MSCV-IRES-GFP/tat. The MSCV-IRES-GFP/tat was cotransfected into 293T cells with pCMV-VSV-G and pUMVC vectors to assemble the recombinant retrovirus. After infection of gliomas U87 cells with equal amount of the recombinant retrovirus, TNF-α, and IL-1β concentrations in the supernatant of U87 cells were determined with ELISA. Results HIV-1 tat genes derived from peripheral spleen and central basal ganglia of the AIDS patient with ADC and the other one without ADC exhibited genetic variations. Tat variations and amino acid mutation sites existed mainly at Tat protein core functional area （38-47aa）. All Tat proteins could induce ug7 cells to produce TNF-α and IL-1β, but the level of IL-1β production was different among Tat proteins derived from the ADC patient＇s spleen, basal ganglia, and the non-ADC patient＇s spleen. The level of Tat proteins derived from the ADC patient＇s spleen, basal ganglia, and the non-ADC patient＇s spleen were obviously higher than that from the non-ADC patient＇s basal ganglia. Conclusion Tat protein core functional area （38-47aa） may serve as the key area of enhancing the secretion of IL-1β. This may be related with the neurotoxicity of HIV-1 Tat.展开更多
A high-speed and effective packet scheduling method is crucial to the performance of Gigabit routers. The paper studies the variable-length packet scheduling problem in Gigabit router with crossbar switch fabric and i...A high-speed and effective packet scheduling method is crucial to the performance of Gigabit routers. The paper studies the variable-length packet scheduling problem in Gigabit router with crossbar switch fabric and input queuing, and a scheduling method based on neural network is proposed. For the proposed method, a scheduling system structure fit for the variable-length packet case is presented first, then some rules for scheduling are given. At last, an optimal scheduling method using Hopfield neural network is proposed based on the rules. Furthermore, the paper discusses that the proposed method can be realized by hardware circuit. The simulation result shows the effectiveness of the proposed method.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
Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances.Current encodi...Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances.Current encoding schemes including time-bin,polarization,and orbital angular momentum,suffer from the lack of reconfigurability and thus scalability issues.Here,we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding.Specifically,we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols,and importantly equips each user with a single detector rather than four detectors.This minimizes massively the resource overhead,reduces the dark count contribution,vulnerability to detector side-channel attacks,and the detector imbalance,hence providing an enhanced security.Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead,enabling increased secret key rate and reconfigurable multi-user operations.In perspective,our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies,and facilitates scalability to large-scale quantum networks.展开更多
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 Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grant No.22240021the Grant-in-Aid for Challenging Exploratory Research under Grant No.21650030
文摘In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
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.
基金The National Natural Science Foundation of China (No.60403027)the Research Foundation for Out-standing Young Teachers, China University of Geosciences(Wuhan) (No.CUGQNL0836)
文摘Mobile ad hoc networks create additional challenges for implementing the group key establishment due to resource constraints on nodes and dynamic changes on topology. The nodes in mobile ad hoc networks are usually low power devices that run on battery power. As a result, the costs of the node resources should be minimized when constructing a group key agreement protocol so that the battery life could be prolonged. To achieve this goal, in this paper we propose a security efficient group key agreement protocol based on Burmester-Desmedt （BD） scheme and layer-cluster group model, referred to as LCKM-BD, which is appropriate for large mobile ad hoe networks. In the layer-cluster group model, BD scheme is employed to establish group key, which can not only meet security demands of mobile ad hoc networks but also improve executing performance. Finally, the proposed protocol LCKM-BD are compared with BD, TGDH （tree-based group Diffe-Hellman）, and GDH （group Diffie-Hellman） group key agreement protocols. The analysis results show that our protocol can significantly decrease both the computational overhead and communication costs with respect to these comparable protocols.
基金supported by the National Natural Science Foundation of China(Grant Nos.71003078and 70833005)sponsored by SRF for ROCS and SEM
文摘Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.
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 Science&Technology Development Program of Shandong Province(Grant No.2007GG30002003)
文摘Objective To explore the role of HIV-1 tat gene variations in AIDS dementia complex （ADC） pathogenesis. Methods HIV-1 tat genes derived from peripheral spleen and central basal ganglia of an AIDS patient with ADC and an AIDS patient without ADC were cloned for sequence analysis. HIV-1 tat gene sequence alignment was performed by using CLUSTAL W and the phylogentic analysis was conducted by using Neighbor-joining with MEGA4 software. All tat genes were used to construct recombinant retroviral expressing vector MSCV-IRES-GFP/tat. The MSCV-IRES-GFP/tat was cotransfected into 293T cells with pCMV-VSV-G and pUMVC vectors to assemble the recombinant retrovirus. After infection of gliomas U87 cells with equal amount of the recombinant retrovirus, TNF-α, and IL-1β concentrations in the supernatant of U87 cells were determined with ELISA. Results HIV-1 tat genes derived from peripheral spleen and central basal ganglia of the AIDS patient with ADC and the other one without ADC exhibited genetic variations. Tat variations and amino acid mutation sites existed mainly at Tat protein core functional area （38-47aa）. All Tat proteins could induce ug7 cells to produce TNF-α and IL-1β, but the level of IL-1β production was different among Tat proteins derived from the ADC patient＇s spleen, basal ganglia, and the non-ADC patient＇s spleen. The level of Tat proteins derived from the ADC patient＇s spleen, basal ganglia, and the non-ADC patient＇s spleen were obviously higher than that from the non-ADC patient＇s basal ganglia. Conclusion Tat protein core functional area （38-47aa） may serve as the key area of enhancing the secretion of IL-1β. This may be related with the neurotoxicity of HIV-1 Tat.
文摘A high-speed and effective packet scheduling method is crucial to the performance of Gigabit routers. The paper studies the variable-length packet scheduling problem in Gigabit router with crossbar switch fabric and input queuing, and a scheduling method based on neural network is proposed. For the proposed method, a scheduling system structure fit for the variable-length packet case is presented first, then some rules for scheduling are given. At last, an optimal scheduling method using Hopfield neural network is proposed based on the rules. Furthermore, the paper discusses that the proposed method can be realized by hardware circuit. The simulation result shows the effectiveness of the proposed method.
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.
基金Open Access funding enabled and organized by Projekt DEAL.
文摘Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances.Current encoding schemes including time-bin,polarization,and orbital angular momentum,suffer from the lack of reconfigurability and thus scalability issues.Here,we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding.Specifically,we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols,and importantly equips each user with a single detector rather than four detectors.This minimizes massively the resource overhead,reduces the dark count contribution,vulnerability to detector side-channel attacks,and the detector imbalance,hence providing an enhanced security.Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead,enabling increased secret key rate and reconfigurable multi-user operations.In perspective,our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies,and facilitates scalability to large-scale quantum networks.
