A new centrality measure based on neighbor loop structure for network dismantling

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.

Classifier for centrality determination with zero-degree calorimeter at the cooling-storage-ring external-target experiment

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.

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

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.

AIGCrank:A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

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.

BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

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%.

Voice and Heart of Darkness:Centrality and Discourse in Colonialism

My essay on Heart of Darkness explores Conrad’s dramatization of the voice and heart dynamic in the novella as a penetration of the deep-lying rationale of colonial operation and more generally the human condition at the turn of the 20th century.Three different forms of assumed centrality in the novella-capital,Eurocentrism,and the African interior as detrimental void symbolized by the heart of darkness form concentric circles that interconnect and work in concert as the core colonial ideology and produce a sense of incongruity and absurdity.The voice of this colonial ideological apparatus disguised as the universal subject of humanity and rationality finds its best expression in the eloquence of Kurtz,the specimen of culture seduced by its very opposite-the wilderness-right in the heart of darkness.“The horror”,as his final verdict of life as a European with real intent of living on his own terms at the turn of the 20th century,sums up the predicament of a civilization with too much culture and self-absorption for its own good.

Centrality measure of complex networks based on resource flow

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.

Nodes and layers PageRank centrality for multilayer networks

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.

Terrorist Networks, Network Energy and Node Removal: A New Measure of Centrality Based on Laplacian Energy

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.

Abnormal resting-state functional network centrality in patients with high myopia： evidence from a voxel-wise degree centrality analysis

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.

Road Centrality and Landscape Spatial Patterns in Wuhan Metropolitan Area,China

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.

Altered brain network centrality in patients with retinal vein occlusion: a resting-state fMRI study

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.

The Effects of Centrality Ordering in Label Propagation for Community Detection

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.

Village Network Centrality in Rural Tourism Destination:A Case from Yesanpo Tourism Area,China

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.

Network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

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.

An Energy-Based Centrality for Electrical Networks

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.

Factors That Affect the Centrality Controllability of Scale-Free Networks

The influence of a node in a network can be characterized by its macroscopic properties such as eigenvector centrality. An issue of significant theoretical and practical interest is to modify the influence or roles of the nodes in a network, and recent advances indicate that this can be achieved by just controlling a subset of nodes： the socalled controllers. However, the relationship between the structural properties of a network and its controllability, e.g., the control of node importance, is still not well understood. Here we systematically＂ explore this relationship by constructing scale-free networks with a fixed degree sequence and tunable network characteristics. We calculate the relative size （nc＊） of the minimai controlling set required to controi the importance of each individual node in a network. It is found that while clustering has no significant impact on nc＊, changes in degree-degree correlations, heterogeneity and the average degree of networks demonstrate a discernible impact on its controllability.

Centrality Measures Based on Matrix Functions

Network is considered naturally as a wide range of different contexts, such as biological systems, social relationships as well as various technological scenarios. Investigation of the dynamic phenomena taking place in the network, determination of the structure of the network and community and description of the interactions between various elements of the network are the key issues in network analysis. One of the huge network structure challenges is the identification of the node(s) with an outstanding structural position within the network. The popular method for doing this is to calculate a measure of centrality. We examine node centrality measures such as degree, closeness, eigenvector, Katz and subgraph centrality for undirected networks. We show how the Katz centrality can be turned into degree and eigenvector centrality by considering limiting cases. Some existing centrality measures are linked to matrix functions. We extend this idea and examine the centrality measures based on general matrix functions and in particular, the logarithmic, cosine, sine, and hyperbolic functions. We also explore the concept of generalised Katz centrality. Various experiments are conducted for different networks generated by using random graph models. The results show that the logarithmic function in particular has potential as a centrality measure. Similar results were obtained for real-world networks.

Retail Outlets and City Centrality: Perspectives from a Sub-Saharan African City

Sub-Saharan African cities are uniquely characterized by retail competition influencing outlet location.This work focused on revealing retail outlet location in Uyo and its relationship with distances from the Central Business Districts.Distance was measured along the six major arterial roads that link the city to the central business districts by the use of Google Map[5]Distance Calculator and itouch maps[7]technology.Six arteries were divided into four distinct spaces in kilometers namely 0-1km,1-2km,2-3km,3-4km.Retail Outlets were grouped into ten classes.Data was analysed using distance in kilometers from the Central Business District as variable Y1.The independent variables X1,X2,X3,X4,X5 and X6 were the six major arteries represented by the location of each specific retail outlet group.Utilising SPSS version 20 software the results reflected centre a 47.9 percent variation in retail outlets location with correlation coefficient(R)of 69.2%revealing a strong relationship between the distances from the Central Business Districts and the location of retail outlets located across the six major arteries.H1 was accepted which states a significant relationship in the location of retail outlets as distance increase from the city centre across the linkages.This confirms a strong intensity of location of retail outlets in the city centre with a gradual decline as distance increase from the centre.Although there was an increase in intensity of retail outlets in junctions away from the Central Business Districts according to multiple nuclei concept.It is recommended that urban expansion through growth poles.Aim:To access the relationship between retail outlets location and distance from central business district in a Uyo.Research Questions:This work provided answers to the following questions:1.Where are these retail outlets located in the city space?2.What is the relationship between Retail outlets and the distance from the City Centre?Objectives:1.To explain the distribution of location of retail outlets in city space.2.To reveal the relationship between Retail Outlets and distance from the City Centre.Hypothesis:There is no significant relationship between the distance from the central business district and the location of retail outlets.

Study of ¹²CC Collisions at 4.5 A GeV to Define Centrality

Using a statistical method which is based on random matrix theory, the results for the nearest-neighbor energy spacing distributions E(S) obtained from experimental as well as from computational data have been selected for review study. The obtained results confirm that the energy spacing correlation between secondary charged particles depends upon the charged particles multiplicity and central collisions are also associated with charged particles multiplicity.