BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To...BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.展开更多
Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network f...Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.展开更多
One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms...One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms focus on single feature or manually extract the attributes,which occasionally results in the failure to fully capture the vertex’s importance.A new vertex centrality approach based on symmetric nonnegative matrix factorization(SNMF),called VCSNMF,is proposed in this paper.For highlight the characteristics of a network,the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination.First,SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix.Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network,otherwise obtained by the factor matrix on the hyperspace.Finally,VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices.To verify the effectiveness of the VCSNMF criterion,eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks.The experimental results assert the superiority of the method.展开更多
A new centrality measure for complex networks, called resource flow centrality, is pro- posed in this paper. This centrality measure is based on the concept of the resource flow in net- works. It not only can be appli...A new centrality measure for complex networks, called resource flow centrality, is pro- posed in this paper. This centrality measure is based on the concept of the resource flow in net- works. It not only can be applied to the connected networks, but also the disconnected networks. Moreover, it overcomes some disadvantages of several common centrality measures. The perform- ance of the proposed measure is compared with some standard centrality measures using a classic dataset and the results indicate the proposed measure performs more reasonably. The statistical dis- tribution of the proposed centrality is investigated by experiments on large scale computer generated graphs and two networks from the real world.展开更多
Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors infl...Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors influencing centrality were measured.In addition,data for the population flow were used to analyze the relationships between cities and to verify centrality.The results showed that:(1)The pattern of centrality conforms closely to the pole-axis theory and the central geography theory.Two axes,corresponding to the Yangtze River and the Shanghai-Kunming railway line,interconnect cities of different classes.On the whole,the downstream cities have higher centrality,well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches.(2)The economic scale and size of the population play a fundamental role in the centrality of cities,and other factors reflect differences due to different city classes.For most of the coastal cities or the capital cities in the central and western regions,factors that require long-term development such as industrial facilities,consumption,research and education provide the main competitive advantages.For cities that are lagging behind in development,transportation facilities,construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness.(3)The mobility of city populations has a significant correlation with the centrality score,the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86(P<0.01).The population flow is mainly between high-class cities,or high-class and low-class cities,reflecting the high centrality and huge radiating effects of high-class cities.Furthermore,the cities in the YREB are closely linked to Guangdong and Beijing,reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's enormous influence as the national political and cultural center,respectively.展开更多
In this paper, we propose a new centrality algorithm that can simultaneously rank the nodes and layers of multilayer networks, referred to as the MRFNL centrality. The centrality of nodes and layers are obtained by de...In this paper, we propose a new centrality algorithm that can simultaneously rank the nodes and layers of multilayer networks, referred to as the MRFNL centrality. The centrality of nodes and layers are obtained by developing a novel iterative algorithm for computing a set of tensor equations. Under some conditions, the existence and uniqueness of this centrality were proven by applying the Brouwer fixed point theorem. Furthermore, the convergence of the proposed iterative algorithm was established. Finally, numerical experiments on a simple multilayer network and two real-world multilayer networks(i.e., Pierre Auger Collaboration and European Air Transportation Networks) are proposed to illustrate the effectiveness of the proposed algorithm and to compare it to other existing centrality measures.展开更多
In this work we propose a centrality measure for networks, which we refer to as Laplacian centrality, that provides a general framework for the centrality of a vertex based on the idea that the importance (or centrali...In this work we propose a centrality measure for networks, which we refer to as Laplacian centrality, that provides a general framework for the centrality of a vertex based on the idea that the importance (or centrality) of a vertex is related to the ability of the network to respond to the deactivation or removal of that vertex from the network. In particular, the Laplacian centrality of a vertex is defined as the relative drop of Laplacian energy caused by the deactivation of this vertex. The Laplacian energy of network G with?n?vertices is defined as , where ?is the eigenvalue of the Laplacian matrix of G. Other dynamics based measures such as that of Masuda and Kori and PageRank compute the importance of a node by analyzing the way paths pass through a node while our measure captures this information as well as the way these paths are “redistributed” when the node is deleted. The validity and robustness of this new measure are illustrated on two different terrorist social network data sets and 84 networks in James Moody’s Add Health in school friendship nomination data, and is compared with other standard centrality measures.展开更多
AIM： To investigate the functional networks underlying the brain-activity changes of patients with high myopia using the voxel-wise degree centrality（DC） method.METHODS： In total, 38 patients with high myopia（HM...AIM： To investigate the functional networks underlying the brain-activity changes of patients with high myopia using the voxel-wise degree centrality（DC） method.METHODS： In total, 38 patients with high myopia（HM）（17 males and 21 females）, whose binocular refractive diopter were-6.00 to-7.00 D, and 38 healthy controls（17 males and 21 females）, closely matched in age, sex, and education levels, participated in the study. Spontaneous brain activities were evaluated using the voxel-wise DC method. The receiver operating characteristic curve was measured to distinguish patients with HM from healthy controls. Correlation analysis was used to explore the relationship between the observed mean DC values of the different brain areas and the behavioral performance.RESULTS： Compared with healthy controls, HM patients had significantly decreased DC values in the right inferior frontal gyrus/insula, right middle frontal gyrus, and right supramarginal/inferior parietal lobule（P〈0.05）. In contrast, HM patients had significantly increased DC values in the right cerebellum posterior lobe, left precentral gyrus/postcentral gyrus, and right middle cingulate gyrus（P〈0.05）. However, no relationship was found between the observed mean DC values of the different brain areas and the behavioral performance（P〉0.05）.CONCLUSION： HM is associated with abnormalities in many brain regions, which may indicate the neural mechanisms of HM. The altered DC values may be used as a useful biomarker for the brain activity changes in HM patients.展开更多
Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the...Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the relationships between road centrality and landscape patterns in the Wuhan Metropolitan Area,China.The densities of centrality measures,including closeness,betweenness,and straightness,are calculated by kernel density estimation(KDE).The landscape patterns are characterized by four landscape metrics,including percentage of landscape(PLAND),Shannon′s diversity index(SHDI),mean patch size(MPS),and mean shape index(MSI).Spearman rank correlation analysis is then used to quantify their relationships at both landscape and class levels.The results show that the centrality measures can reflect the hierarchy of road network as they associate with road grade.Further analysis exhibit that as centrality densities increase,the whole landscape becomes more fragmented and regular.At the class level,the forest gradually decreases and becomes fragmented,while the construction land increases and turns to more compact.Therefore,these findings indicate that the ability and potential applications of centrality densities estimated by KDE in quantifying the relationships between roads and landscapes,can provide detailed information and valuable guidance for transportation and land-use planning as well as a new insight into ecological effects of roads.展开更多
The zero-degree calorimeter(ZDC)plays a crucial role toward determining the centrality in the Cooling-Storage-Ring External-target Experiment(CEE)at the Heavy Ion Research Facility in Lanzhou.A boosted decision tree(B...The zero-degree calorimeter(ZDC)plays a crucial role toward determining the centrality in the Cooling-Storage-Ring External-target Experiment(CEE)at the Heavy Ion Research Facility in Lanzhou.A boosted decision tree(BDT)multi-classification algorithm was employed to classify the centrality of the collision events based on the raw features from ZDC such as the number of fired channels and deposited energy.The data from simulated^(238)U+^(238)U collisions at 500 MeV∕u,generated by the IQMD event generator and subsequently modeled using the GEANT4 package,were employed to train and test the BDT model.The results showed the high accuracy of the multi-classification model adopted in ZDC for centrality determination,which is robust against variations in different factors of detector geometry and response.This study demon-strates the good performance of CEE-ZDC in determining the centrality in nucleus-nucleus collisions.展开更多
In many cases randomness in community detection algorithms has been avoided due to issues with stability. Indeed replacing random ordering with centrality rankings has improved the performance of some techniques such ...In many cases randomness in community detection algorithms has been avoided due to issues with stability. Indeed replacing random ordering with centrality rankings has improved the performance of some techniques such as Label Propagation Algorithms. This study evaluates the effects of such orderings on the Speaker-listener Label Propagation Algorithm or SLPA, a modification of LPA which has already been stabilized through alternate means. This study demonstrates that in cases where stability has been achieved without eliminating randomness, the result of removing random ordering is over fitting and bias. The results of testing seven various measures of centrality in conjunction with SLPA across five social network graphs indicate that while certain measures outperform random orderings on certain graphs, random orderings have the highest overall accuracy. This is particularly true when strict orderings are used in each run. These results indicate that the more evenly distributed solution space which results from complete random ordering is more valuable than the more targeted search that results from centrality orderings.展开更多
AIM:To explore the intrinsic brain activity variations in retinal vein occlusion(RVO)subjects by using the voxel-wise degree centrality(DC)technique.METHODS:Twenty-one subjects with RVO and twentyone healthy controls(...AIM:To explore the intrinsic brain activity variations in retinal vein occlusion(RVO)subjects by using the voxel-wise degree centrality(DC)technique.METHODS:Twenty-one subjects with RVO and twentyone healthy controls(HCs)were enlisted and underwent the resting-state functional magnetic resonance imaging(rs-f MRI)examination.The spontaneous cerebrum activity variations were inspected using the DC technology.The receiver operating characteristic(ROC)curve was implemented to distinguish the DC values of RVOs from HCs.The relationships between DC signal of definite regions of interest and the clinical characteristics in RVO group were evaluated by Pearson’s correlation analysis.RESULTS:RVOs showed notably higher DC signals in right superior parietal lobule,middle frontal gyrus and left precuneus,but decreased DC signals in left middle temporal gyrus and bilateral anterior cingulated(BAC)when comparing with HCs.The mean DC value of RVOs in the BAC were negatively correlated with the anxiety and depression scale.CONCLUSION:RVO is associated aberrant intrinsic brain activity patterns in several brain areas including painrelated as well as visual-related regions,which might assist to reveal the latent neural mechanisms.展开更多
By using the recent spatially dependent nuclear PDF set EPS09 s, we investigated the centrality-dependent Cold Nuclear Matter(CNM) effects for neutral π, η mesons and inclusive jets at RHIC in d+Au collisions and at...By using the recent spatially dependent nuclear PDF set EPS09 s, we investigated the centrality-dependent Cold Nuclear Matter(CNM) effects for neutral π, η mesons and inclusive jets at RHIC in d+Au collisions and at LHC in p+Pb collisions. The nuclear modification factors as functions of transverse momentum are plotted at different centralities bins respectively. At all fixed centralities, the nuclear modification factors show no significant suppressions,contrast to the strong suppressions observed for central Au+Au collisions. Our results are consistent with the PHENIX preliminary Data in minimum bias and central d+Au collisions. The LHC experimental Data also support our predictions for both single inclusive hadron and inclusive jets productions in central p+Pb collisions. And the centrality dependence of the nuclear suppressions for all the observations in our calculations are lower than the RHIC and LHC Data.展开更多
The analysis of urban drainage networks(UDNs)is one of the most important topics in the study of water systems.The interest in strategies aimed at analyzing the impacts of sewer pipes failure on the urban drainage sys...The analysis of urban drainage networks(UDNs)is one of the most important topics in the study of water systems.The interest in strategies aimed at analyzing the impacts of sewer pipes failure on the urban drainage system operation is growing,and the need of developing methodologies aimed at vulnerability assessment and system management is increasingly important.To this purpose,the present work shows and discusses the use of complex network theory.In particular,the recently developed relevance‐based centrality metrics have been used to classify UDNs and to identify the most critical pipes.First,the relevancebased degree is applied to the direct graph of the drainage network to classify the systems.Afterward,the relevance‐based edge betweenness is used for ranking the importance,that is,the criticality with respect to fluxes,for the pipes.The relevance‐based metrics assign importance to the network elements(pipes and nodes),considering both the intrinsic relevance of nodes and the network connectivity structure.Results provide useful information to support pipe maintenance programs to be prepared for malfunctioning events by means of a criticality analysis in advance.The relevance‐based metrics are presented by using the direct graph of a simple example network,and they are then applied both to a benchmark and a real urban drainage system to show the effectiveness even for real systems.展开更多
With the development of rural tourism, the cooperation of villages has become very important.Identifying the status and importance of each village can contribute to better understanding of the integrated rural tourism...With the development of rural tourism, the cooperation of villages has become very important.Identifying the status and importance of each village can contribute to better understanding of the integrated rural tourism management and sustainable rural tourism development. The research focused on 46 villages of Yesanpo scenic spot in China(39°35'-40°north latitude, and 115°16'- 115°30' east longitude). Integrating the method of Geographical Information System(GIS) and social network analysis, the spatial centrality and interrelation of each village in Yesanpo tourism destination were evaluated. The results showed that Xinggezhuang is the spatial core village of the whole 46 villages in Yesanpo tourism areas; Xinggezhuang, Nanzhuang, Zhenchang, Daze, Liujiahe and Zishikou are sub-core villages of the six tourism spots. Magezhuang, Ximagezhuang, Eyu, Zishikou, Daze, Shangzhuang, Zhenchang and Xiazhuang should be support of the core villages, which provide subsidiary services and connects with other nodes. The results also indicated that the study of the village centrality will contribute to build an integrated hierarchy structure and to provide sufficient basis for further development of rural tourism destination.展开更多
Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat...Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.展开更多
The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds man...The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.展开更多
The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based fea...The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.展开更多
Wireless Sensor Network(WSN)is an important part of the Internet of Things(IoT),which are used for information exchange and communication between smart objects.In practical applications,WSN lifecycle can be influenced...Wireless Sensor Network(WSN)is an important part of the Internet of Things(IoT),which are used for information exchange and communication between smart objects.In practical applications,WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption,etc.In order to overcome these problems,a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network.Also,a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics.Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost,and the invulnerability of the network is effectively enhanced.展开更多
We present an energy-based method to estimate centrality in electrical networks. Here the energy between a pair of vertices denotes by the effective resistance between them. If there is only one generation and one loa...We present an energy-based method to estimate centrality in electrical networks. Here the energy between a pair of vertices denotes by the effective resistance between them. If there is only one generation and one load, then the centrality of an edge in our method is the difference between the energy of network after deleting the edge and that of the original network. Compared with the local current-flow betweenness on the IEEE 14-bus system, we have an interesting discovery that our proposed centrality is closely related to it in the sense of that the significance of edges under the two measures are very similar.展开更多
基金Supported by Hubei Provincial Department of Science and Technology Natural Fund,No.2024AFC056the Open Fund of the Mental Health Research Institute at Three Gorges University,No.YCXL-23-11.
文摘BACKGROUND Major depressive disorder(MDD)with comorbid anxiety is an intricate psychiatric condition,but limited research is available on the degree centrality(DC)between anxious MDD and nonanxious MDD patients.AIM To examine changes in DC values and their use as neuroimaging biomarkers in anxious and non-anxious MDD patients.METHODS We examined 23 anxious MDD patients,30 nonanxious MDD patients,and 28 healthy controls(HCs)using the DC for data analysis.RESULTS Compared with HCs,the anxious MDD group reported markedly reduced DC values in the right fusiform gyrus(FFG)and inferior occipital gyrus,whereas elevated DC values in the left middle frontal gyrus and left inferior parietal angular gyrus.The nonanxious MDD group exhibited surged DC values in the bilateral cerebellum IX,right precuneus,and opercular part of the inferior frontal gyrus.Unlike the nonanxious MDD group,the anxious MDD group exhibited declined DC values in the right FFG and bilateral calcarine(CAL).Besides,declined DC values in the right FFG and bilateral CAL negatively correlated with anxiety scores in the MDD group.CONCLUSION This study shows that abnormal DC patterns in MDD,especially in the left CAL,can distinguish MDD from its anxiety subtype,indicating a potential neuroimaging biomarker.
基金the National Natural Science Foundation of China under Grants 61871209 and 61901210,in part by Artificial Intelligence and Intelligent Transportation Joint Technical Center of HUST and Hubei Chutian Intelligent Transportation Co.,LTD under project”Intelligent Transportation Operation Monitoring Network and System”.
文摘Nearly all real-world networks are complex networks and usually are in danger of collapse.Therefore,it is crucial to exploit and understand the mechanisms of network attacks and provide better protection for network functionalities.Network dismantling aims to find the smallest set of nodes such that after their removal the network is broken into connected components of sub-extensive size.To overcome the limitations and drawbacks of existing network dismantling methods,this paper focuses on network dismantling problem and proposes a neighbor-loop structure based centrality metric,NL,which achieves a balance between computational efficiency and evaluation accuracy.In addition,we design a novel method combining NL-based nodes-removing,greedy tree-breaking and reinsertion.Moreover,we compare five baseline methods with our algorithm on ten widely used real-world networks and three types of model networks including Erd€os-Renyi random networks,Watts-Strogatz smallworld networks and Barabasi-Albert scale-free networks with different network generation parameters.Experimental results demonstrate that our proposed method outperforms most peer methods by obtaining a minimal set of targeted attack nodes.Furthermore,the insights gained from this study may be of assistance to future practical research into real-world networks.
基金the National Natural Science Foundation of China(Nos.11361033 and 11861045)。
文摘One of the most important problems in complex networks is to identify the influential vertices for understanding and controlling of information diffusion and disease spreading.Most of the current centrality algorithms focus on single feature or manually extract the attributes,which occasionally results in the failure to fully capture the vertex’s importance.A new vertex centrality approach based on symmetric nonnegative matrix factorization(SNMF),called VCSNMF,is proposed in this paper.For highlight the characteristics of a network,the adjacency matrix and the degree matrix are fused to represent original data of the network via a weighted linear combination.First,SNMF automatically extracts the latent characteristics of vertices by factorizing the established original data matrix.Then we prove that each vertex’s composite feature which is constructed with one-dimensional factor matrix can be approximated as the term of eigenvector associated with the spectral radius of the network,otherwise obtained by the factor matrix on the hyperspace.Finally,VCSNMF integrates the composite feature and the topological structure to evaluate the performance of vertices.To verify the effectiveness of the VCSNMF criterion,eight existing centrality approaches are used as comparison measures to rank influential vertices in ten real-world networks.The experimental results assert the superiority of the method.
基金Supported by the National Natural Science Foundation of China(61272119,61203372)
文摘A new centrality measure for complex networks, called resource flow centrality, is pro- posed in this paper. This centrality measure is based on the concept of the resource flow in net- works. It not only can be applied to the connected networks, but also the disconnected networks. Moreover, it overcomes some disadvantages of several common centrality measures. The perform- ance of the proposed measure is compared with some standard centrality measures using a classic dataset and the results indicate the proposed measure performs more reasonably. The statistical dis- tribution of the proposed centrality is investigated by experiments on large scale computer generated graphs and two networks from the real world.
基金National Natural Science Foundation of China,No.41871176The“Hua Bo”Plan of Central China Normal UniversityPostgraduate Education Innovation Subsidy Project of Central China Normal University,No.2018CXZZ004。
文摘Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors influencing centrality were measured.In addition,data for the population flow were used to analyze the relationships between cities and to verify centrality.The results showed that:(1)The pattern of centrality conforms closely to the pole-axis theory and the central geography theory.Two axes,corresponding to the Yangtze River and the Shanghai-Kunming railway line,interconnect cities of different classes.On the whole,the downstream cities have higher centrality,well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches.(2)The economic scale and size of the population play a fundamental role in the centrality of cities,and other factors reflect differences due to different city classes.For most of the coastal cities or the capital cities in the central and western regions,factors that require long-term development such as industrial facilities,consumption,research and education provide the main competitive advantages.For cities that are lagging behind in development,transportation facilities,construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness.(3)The mobility of city populations has a significant correlation with the centrality score,the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86(P<0.01).The population flow is mainly between high-class cities,or high-class and low-class cities,reflecting the high centrality and huge radiating effects of high-class cities.Furthermore,the cities in the YREB are closely linked to Guangdong and Beijing,reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's enormous influence as the national political and cultural center,respectively.
基金Project supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province,China(Grant No.AE91313/001/016)the National Natural Science Foundation of China(Grant No.11701097)the Natural Science Foundation of Jiangxi Province,China(Grant No.20161BAB212055)
文摘In this paper, we propose a new centrality algorithm that can simultaneously rank the nodes and layers of multilayer networks, referred to as the MRFNL centrality. The centrality of nodes and layers are obtained by developing a novel iterative algorithm for computing a set of tensor equations. Under some conditions, the existence and uniqueness of this centrality were proven by applying the Brouwer fixed point theorem. Furthermore, the convergence of the proposed iterative algorithm was established. Finally, numerical experiments on a simple multilayer network and two real-world multilayer networks(i.e., Pierre Auger Collaboration and European Air Transportation Networks) are proposed to illustrate the effectiveness of the proposed algorithm and to compare it to other existing centrality measures.
文摘In this work we propose a centrality measure for networks, which we refer to as Laplacian centrality, that provides a general framework for the centrality of a vertex based on the idea that the importance (or centrality) of a vertex is related to the ability of the network to respond to the deactivation or removal of that vertex from the network. In particular, the Laplacian centrality of a vertex is defined as the relative drop of Laplacian energy caused by the deactivation of this vertex. The Laplacian energy of network G with?n?vertices is defined as , where ?is the eigenvalue of the Laplacian matrix of G. Other dynamics based measures such as that of Masuda and Kori and PageRank compute the importance of a node by analyzing the way paths pass through a node while our measure captures this information as well as the way these paths are “redistributed” when the node is deleted. The validity and robustness of this new measure are illustrated on two different terrorist social network data sets and 84 networks in James Moody’s Add Health in school friendship nomination data, and is compared with other standard centrality measures.
基金Supported by National Natural Science Foundation of China (No.81760179 No.81360151)+2 种基金Natural Science Foundation of Jiangxi Province (No.20171BAB205046)Jiangxi Province Education Department Key Foundation (No. GJJ160033)Health Development Planning Commission Science Foundation of Jiangxi Province (No.20185118)
文摘AIM： To investigate the functional networks underlying the brain-activity changes of patients with high myopia using the voxel-wise degree centrality（DC） method.METHODS： In total, 38 patients with high myopia（HM）（17 males and 21 females）, whose binocular refractive diopter were-6.00 to-7.00 D, and 38 healthy controls（17 males and 21 females）, closely matched in age, sex, and education levels, participated in the study. Spontaneous brain activities were evaluated using the voxel-wise DC method. The receiver operating characteristic curve was measured to distinguish patients with HM from healthy controls. Correlation analysis was used to explore the relationship between the observed mean DC values of the different brain areas and the behavioral performance.RESULTS： Compared with healthy controls, HM patients had significantly decreased DC values in the right inferior frontal gyrus/insula, right middle frontal gyrus, and right supramarginal/inferior parietal lobule（P〈0.05）. In contrast, HM patients had significantly increased DC values in the right cerebellum posterior lobe, left precentral gyrus/postcentral gyrus, and right middle cingulate gyrus（P〈0.05）. However, no relationship was found between the observed mean DC values of the different brain areas and the behavioral performance（P〉0.05）.CONCLUSION： HM is associated with abnormalities in many brain regions, which may indicate the neural mechanisms of HM. The altered DC values may be used as a useful biomarker for the brain activity changes in HM patients.
基金Under the auspices of National Key Technology Research and Development Program of China(No.2012BAH28B02)
文摘Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the relationships between road centrality and landscape patterns in the Wuhan Metropolitan Area,China.The densities of centrality measures,including closeness,betweenness,and straightness,are calculated by kernel density estimation(KDE).The landscape patterns are characterized by four landscape metrics,including percentage of landscape(PLAND),Shannon′s diversity index(SHDI),mean patch size(MPS),and mean shape index(MSI).Spearman rank correlation analysis is then used to quantify their relationships at both landscape and class levels.The results show that the centrality measures can reflect the hierarchy of road network as they associate with road grade.Further analysis exhibit that as centrality densities increase,the whole landscape becomes more fragmented and regular.At the class level,the forest gradually decreases and becomes fragmented,while the construction land increases and turns to more compact.Therefore,these findings indicate that the ability and potential applications of centrality densities estimated by KDE in quantifying the relationships between roads and landscapes,can provide detailed information and valuable guidance for transportation and land-use planning as well as a new insight into ecological effects of roads.
基金This work was supported in part by the National Nature Science Foundation of China(NSFC)(Nos.11927901 and 12175084)the National Key Research and Development Program of China(Nos.2020YFE0202002 and 2022YFA1604900)the Fundamental Research Funds for the Central Universities(No.CCNU22QN005).
文摘The zero-degree calorimeter(ZDC)plays a crucial role toward determining the centrality in the Cooling-Storage-Ring External-target Experiment(CEE)at the Heavy Ion Research Facility in Lanzhou.A boosted decision tree(BDT)multi-classification algorithm was employed to classify the centrality of the collision events based on the raw features from ZDC such as the number of fired channels and deposited energy.The data from simulated^(238)U+^(238)U collisions at 500 MeV∕u,generated by the IQMD event generator and subsequently modeled using the GEANT4 package,were employed to train and test the BDT model.The results showed the high accuracy of the multi-classification model adopted in ZDC for centrality determination,which is robust against variations in different factors of detector geometry and response.This study demon-strates the good performance of CEE-ZDC in determining the centrality in nucleus-nucleus collisions.
文摘In many cases randomness in community detection algorithms has been avoided due to issues with stability. Indeed replacing random ordering with centrality rankings has improved the performance of some techniques such as Label Propagation Algorithms. This study evaluates the effects of such orderings on the Speaker-listener Label Propagation Algorithm or SLPA, a modification of LPA which has already been stabilized through alternate means. This study demonstrates that in cases where stability has been achieved without eliminating randomness, the result of removing random ordering is over fitting and bias. The results of testing seven various measures of centrality in conjunction with SLPA across five social network graphs indicate that while certain measures outperform random orderings on certain graphs, random orderings have the highest overall accuracy. This is particularly true when strict orderings are used in each run. These results indicate that the more evenly distributed solution space which results from complete random ordering is more valuable than the more targeted search that results from centrality orderings.
文摘AIM:To explore the intrinsic brain activity variations in retinal vein occlusion(RVO)subjects by using the voxel-wise degree centrality(DC)technique.METHODS:Twenty-one subjects with RVO and twentyone healthy controls(HCs)were enlisted and underwent the resting-state functional magnetic resonance imaging(rs-f MRI)examination.The spontaneous cerebrum activity variations were inspected using the DC technology.The receiver operating characteristic(ROC)curve was implemented to distinguish the DC values of RVOs from HCs.The relationships between DC signal of definite regions of interest and the clinical characteristics in RVO group were evaluated by Pearson’s correlation analysis.RESULTS:RVOs showed notably higher DC signals in right superior parietal lobule,middle frontal gyrus and left precuneus,but decreased DC signals in left middle temporal gyrus and bilateral anterior cingulated(BAC)when comparing with HCs.The mean DC value of RVOs in the BAC were negatively correlated with the anxiety and depression scale.CONCLUSION:RVO is associated aberrant intrinsic brain activity patterns in several brain areas including painrelated as well as visual-related regions,which might assist to reveal the latent neural mechanisms.
基金Supported by Ministry of Science and Technology in China under Grant Nos.2014CB845404,2014DFG02050by Natural Science Foundation of China under Grant Nos.11322546,11435004,11221504
文摘By using the recent spatially dependent nuclear PDF set EPS09 s, we investigated the centrality-dependent Cold Nuclear Matter(CNM) effects for neutral π, η mesons and inclusive jets at RHIC in d+Au collisions and at LHC in p+Pb collisions. The nuclear modification factors as functions of transverse momentum are plotted at different centralities bins respectively. At all fixed centralities, the nuclear modification factors show no significant suppressions,contrast to the strong suppressions observed for central Au+Au collisions. Our results are consistent with the PHENIX preliminary Data in minimum bias and central d+Au collisions. The LHC experimental Data also support our predictions for both single inclusive hadron and inclusive jets productions in central p+Pb collisions. And the centrality dependence of the nuclear suppressions for all the observations in our calculations are lower than the RHIC and LHC Data.
文摘The analysis of urban drainage networks(UDNs)is one of the most important topics in the study of water systems.The interest in strategies aimed at analyzing the impacts of sewer pipes failure on the urban drainage system operation is growing,and the need of developing methodologies aimed at vulnerability assessment and system management is increasingly important.To this purpose,the present work shows and discusses the use of complex network theory.In particular,the recently developed relevance‐based centrality metrics have been used to classify UDNs and to identify the most critical pipes.First,the relevancebased degree is applied to the direct graph of the drainage network to classify the systems.Afterward,the relevance‐based edge betweenness is used for ranking the importance,that is,the criticality with respect to fluxes,for the pipes.The relevance‐based metrics assign importance to the network elements(pipes and nodes),considering both the intrinsic relevance of nodes and the network connectivity structure.Results provide useful information to support pipe maintenance programs to be prepared for malfunctioning events by means of a criticality analysis in advance.The relevance‐based metrics are presented by using the direct graph of a simple example network,and they are then applied both to a benchmark and a real urban drainage system to show the effectiveness even for real systems.
基金supported by the Humanities and Social Science Research Foundation Project of Tianjin Higher Colleges and Universities (No.20142112)
文摘With the development of rural tourism, the cooperation of villages has become very important.Identifying the status and importance of each village can contribute to better understanding of the integrated rural tourism management and sustainable rural tourism development. The research focused on 46 villages of Yesanpo scenic spot in China(39°35'-40°north latitude, and 115°16'- 115°30' east longitude). Integrating the method of Geographical Information System(GIS) and social network analysis, the spatial centrality and interrelation of each village in Yesanpo tourism destination were evaluated. The results showed that Xinggezhuang is the spatial core village of the whole 46 villages in Yesanpo tourism areas; Xinggezhuang, Nanzhuang, Zhenchang, Daze, Liujiahe and Zishikou are sub-core villages of the six tourism spots. Magezhuang, Ximagezhuang, Eyu, Zishikou, Daze, Shangzhuang, Zhenchang and Xiazhuang should be support of the core villages, which provide subsidiary services and connects with other nodes. The results also indicated that the study of the village centrality will contribute to build an integrated hierarchy structure and to provide sufficient basis for further development of rural tourism destination.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62162040 and 11861045)。
文摘Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.
基金the National Social Science Foundation of China(Grant Nos.21BGL217 and 18AZD005)the National Natural Science Foundation of China(Grant Nos.71874108 and 11871328)。
文摘The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.
文摘The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet.Bot detection using machine learning(ML)with flow-based features has been extensively studied in the literature.Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features ofmalicious hosts.Recently,Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations,as graphs provide a real representation of network communications.The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML.We propose BotSward,a graph-based bot detection system that is based on ML.We apply the efficient centrality measures,which are Closeness Centrality(CC),Degree Centrality(CC),and PageRank(PR),and compare them with others used in the state-of-the-art.The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset(CTU-13).The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control(C&C)channel and that cause malicious actions such as phishing,distributed denial-of-service(DDoS)attacks,spam attacks,etc.BotSward is robust to zero-day attacks,suitable for large-scale datasets,and is intended to produce better accuracy than state-of-the-art techniques.The proposed BotSward solution achieved 99%accuracy in botnet attack detection with a false positive rate as low as 0.0001%.
基金This research was funded by the National Natural Science Foundation of China,No.61802010Hundred-Thousand-Ten Thousand Talents Project of Beijing No.2020A28+2 种基金National Social Science Fund of China,No.19BGL184Beijing Excellent Talent Training Support Project for Young Top-Notch Team No.2018000026833TD01Academic Research Projects of Beijing Union University,No.ZK30202103.
文摘Wireless Sensor Network(WSN)is an important part of the Internet of Things(IoT),which are used for information exchange and communication between smart objects.In practical applications,WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption,etc.In order to overcome these problems,a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network.Also,a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics.Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost,and the invulnerability of the network is effectively enhanced.
文摘We present an energy-based method to estimate centrality in electrical networks. Here the energy between a pair of vertices denotes by the effective resistance between them. If there is only one generation and one load, then the centrality of an edge in our method is the difference between the energy of network after deleting the edge and that of the original network. Compared with the local current-flow betweenness on the IEEE 14-bus system, we have an interesting discovery that our proposed centrality is closely related to it in the sense of that the significance of edges under the two measures are very similar.
