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Temporal satellite networks can accurately describe the dynamic process of satellite networks by considering the interaction relationship and interaction sequence between satellite nodes. In addition, the measurement of node importance in satellite networks plays a crucial role in understanding the structure and function of the network. The classical supra-adjacency matrix (SAM) temporal model identifies the key nodes in the temporal network to some extent, which ignores the differences of inter-layer connectivity relationships leading to the inability to reflect the dynamic variations of satellite nodes. Therefore, the evaluation method based on time slot correlation is proposed to measure the importance of satellite nodes in this paper. Firstly, the correlation coefficient of time slot nodes is defined to measure the coupling relationship of adjacent time slots. Secondly, the dynamic supra-adjacency matrix (DSAM) temporal network model is proposed considering the correlation between adjacent time slots and the characteristics of link time. Finally, the node importance ranking results in each time slot and a global perspective are obtained by utilizing the eigenvector centrality. Through experimental simulations of the Iridium and Orbcomm constellations, it is demonstrated that the DSAM method has a more accurate recognition rate and is more stable than the SAM method.

This study aimed to develop an instrument for analysing the attack in high-level volleyball considering the refined variables adjacent to the attack action, the interconnection between direct and indirect actions, the impact of the previous action, and the formation of composite variables. The game complexes were approached as interacting subsystems. The primary goal was to understand the influence of game actions adjacent to the attack. Three matches of a National Women’s 1st Division 2018/2019 (nine sets, 415 plays) were analysed, considering all game complexes (except attack coverage due to reduced occurrence). An Eigenvector Centrality network with 420 nodes and 7367 edges was created. The networks showed that ideal setting conditions, and strong attacks by the outside and opposite hitters without having received a perfect ball, were central in side-out. In transition, we highlight ideal setting conditions, preferences of the outside hitter, quick attacks in Z4, and high balls in Z2. This study is distinct because it considers different aspects related to the systemic review of the game by using composite variables and the actions prior to the attack. Of these results, we highlight that players attacked with slower tempos for the double action of receive-attack, and these were either preferably directed to the parallel or explored the block. Moreover, for the double defence-attack actions, attackers sought the soft spike in Z2, Z4, and Z8; and when two consecutive individual errors occurred, the players did not err but instead continued to attack to force the opponent’s error.

Experienced process plant personnel observe that corrective maintenance work on one asset may often be followed by corrective work on the same asset or connected assets within a short amount of time. This problem is referred to as a cascading failure. Confirming if these events are chronic is difficult given the number of assets and the volume of maintenance and operation data. If cascading events can be identified, preventative measures can be implemented to prevent those cascades, eliminating unnecessary corrective work. This project uses complex network analysis to identify cascading events and where co-occurrence of work is most frequent, in a process plant. Data is drawn from over 50,000 work orders for 5,655 pumps in a mining company over a five-year period. A complex network is produced by connecting assets based on the frequency of co-occurrence of work. Beside the advantages of the visualisation of complex networks, the method produces quantified measures, normalised degree, eigenvector centrality and betweenness centrality, which are used to identify assets with significant impact on other assets. Affected pumps are apparent as communities in the network. This analysis identifies pumps that are “super-spreaders”: pumps who experience corrective maintenance events which lead to corrective maintenance events on other pumps. The model can be tuned to different time windows, for example events within one or seven days. From these insights, changes can be made to operational, maintenance and recording practices to prevent re-occurrence. Of particular note in this data was the occurrence of self–loops in certain pumps and the prevalence of hidden failures in standby pumps.

Link prediction is used to predict future links in complex networks. Traditional methods proposed for link prediction make estimates based on similarity measurements, taking into account only the instant topological structure of the network. However, especially in dynamic networks, the activity of nodes varies over time, so it is not enough to measure similarity from topological properties for a good prediction process. Accordingly, the success rate is low in prediction processes where the power of the nodes in the network is not sufficiently reflected. In this study, a novel link prediction model called “Link Prediction Based on Node Weighting in Complex Networks” is proposed to overcome the mentioned problems. Unlike using weights between nodes, the proposed model is based on calculating the own weights of the nodes and making the link prediction. The weighting process includes factors such as eigenvector centrality, experience, continuity that can reveal the power of nodes over time. The model consists of two parts. The first part is node weighting, which calculates the strength of nodes in the network. The second part is the node-weighted link prediction process, where node weights are used to predict future links. Scientific collaboration data at IEEE Xplore and Australian Open Tennis Tournaments data were used to test the success of the proposed model. In experimental studies conducted in networks created from different time periods, it has been determined that the proposed method gives more successful results than the latest technology methods according to the AUC metric.

The methods to evaluate the robustness of a network have been extensively studied. Such methods often require obtaining traffic equilibrium conditions or solving mathematical problems, and these me...

This paper analyses how network embeddedness affects exploration and exploitation R&D project performance. By developing joint projects, partners and projects are linked to one another and form a network that generates social capital. We examine how the location, which determines the access to information and knowledge within a network of relationships, affects the performance of projects. We consider this question in the setup of exploration and exploitation projects, using a database built from an EU Framework. We find that each of the structural´ embeddedness dimensions (degree, betweenness and eigenvector centrality) have a different impact on the exploration and exploitation project performance. Our empirical analysis extends to project management literature, and social capital theory, by including the effect that the acquisition of external knowledge has on the performance of the project.

The road network is one of the most permanent elements of the physical structure of cities, and the long-term impacts should be considered for effective and efficient road network improvement. It is therefore important to catch up on how the road will be used after construction. However, we do not have much knowledge on the pattern and time lag in the change process of travel demand and supply in the real situation. To explore such changes, this study proposes to evaluate a network with eigenvector centrality (EC) measures that can evaluate the importance of nodes in a network. We believe the analysis based on topological properties by the graph theory is suitable to verify the evolution of road networks. This study analysed long-term changes over 20 years in an actual city to understand the impact of road network improvements. The EC analysis with the weights of traffic indices obtained from survey data evaluates the connectivity of road services on the supply side, and traffic concentration on the demand side.

With advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users’ check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.

Online Social Networks (OSNs) are largely popular. People interact daily on networks such as Facebook, Twitter, YouTube, Email, Messenger, WhatsApp, Google+, Quora, LiveJournal etc. and the types of interaction on these networks are different. The types of content that people share on these networks are also different. Each of the OSNs serves a different purpose, such as sharing multimedia, microblogging, serving as a Question and Answer forum etc. Moreover, the community structure of these networks is also different. However, currently, all OSNs are considered as scale-free network based on the power-law degree distribution nature of these networks. In this paper, the effect of network properties such as density, diameter, degree distribution, global clustering coefficient, local clustering coefficient, homophily, assortativity and other centrality measures such as power-law exponent, eigenvector centrality and closeness centrality on seven online social networks, namely, Facebook, Twitter, YouTube, Email, Google+, Epinions and Gowalla networks are studied. The differences and similarities in the empirical results are analyzed, and the OSNs are divided categories based on the observed differences and similarities in network properties and centrality measures.

Real-world systems, ranging from social to infrastructural, can be abstracted into complex networks. Promoting the spreading of some typical information (for instance, the commercial message, vaccination guidance, innovation, and political movement) on these networked systems can bring benefits to all aspects of society. In this study, we propose an effective edge-based approach for promoting the spreading of information on complex networks. Specifically, we first quantify the potential influence that the addition of each latent edge (that is, edges that do not exist before) could cause to the information spreading dynamics. Then, we strategically add the latent edges to the original networks according to the potential influence of each latent edge. Numerical simulations verify the effectiveness of our strategy and demonstrate that our strategy outperforms several static strategies, namely, adding the latent edges between nodes with the largest degree or eigenvector centrality. This study provides an effective way to promote the spreading of information by modifying the network structure slightly and helps in understanding what a better network structure for the spreading dynamics is. Besides, the theoretical framework established in this study provides inspirations for the further investigations of edge-based promoting strategies for other spreading dynamics.

Non-backtracking centrality was introduced as a way to correct what may be understood as a deficiency in the eigenvector centrality, since the eigenvector centrality in a network can be artificially increased in high-degree nodes (hubs) because a hub is central because its neighbors are central, but these, in turn, are central just because they are hub neighbors. We define the non-backtracking PageRank as a new measure modifying the well-known classic PageRank in order to avoid the possibility of the random walker returning to the node immediately visited (non-backtracking walk). But, as we show, this measure presents a gap and a remarkable difference between the limit of “no penalty for return trips” and the direct calculation of the non-backtracking PageRank. Also, as it is shown in the applications presented, in certain cases this new measure produces notable variations with respect to the classifications obtained by the classic PageRank.

This paper is an attempt to study combinations of tale types using a networks approach and calculating the centrality index of each type (degree, betweenness, eigenvector centrality). The network of tale types seems to take the form of a ‘small world’ with a few types serving as bridges between highly connected sets of tale types. The centrality of each type also seems to depend more on its age than on how widespread it is.

Influential nodes are capable of influencing the Social Network (SN) structure and functions. Influential nodes are not pre-defined and need to be identified to better understand and control the network. Trust of the nodes can be an important indicator of their influence and trusted nodes can also affect other nodes. However, analysis of trust and influence relationship in social network communities needs investigation. This study proposes a SNTrust model to find the trust of nodes in a network using a local and global trust and also investigates trust, influence and their relationship in SN communities. Different SN-based influence evaluation approaches named K-core, closeness centrality, eigenvector centrality, and page rank is used to find influential nodes. We have explored the trust and influence of nodes in their communities as well as in the network. Different standard datasets such as Consulting Company dataset, Freeman EIES dataset, Blogcatalogue dataset, and Facebook groups dataset are used for experimentation and Pearson’s correlation and level of significance (p-value) are used for results evaluation. We found a positive linear correlation between the local trust of a node and its influence. It is found that the nodes which are trusted in the network are highly trusted in their communities. There is a strong linear relationship between the influence of a node in the network and community. Furthermore, it is also observed that nodes that are close to each other in a community have high trust among them. The results show that the proposed SNTrust model performed better in finding trust, influence and their correlation.

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a quasi-species model of GRN evolution. With this model, we prove that–across a broad spectrum of viability and mutation functions–the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium will be proportional to each GRN’s eigenvector centrality in the genotype network. Finally, we determine the structural characteristics of GRNs that are favored in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species models, and thus can provide a mechanistic explanation for the topology of GRNs experiencing various evolutionary forces.

Previous research has demonstrated the influence of street layout on travel behaviour; however, little research has been undertaken to explore these connections using detailed and robust street network analysis or cycling data. In this study, we harness state-of-the-art datasets to model cyclists’ route choice based on a case study of the City of Glasgow, Scotland. First, the social fitness network Strava was used to obtain datasets containing the number of cycling trips on each street intersection for the years 2017 and 2018. Second, we employed a Python toolkit to acquire and analyse the street networks. OSMnx was subsequently employed to quantify several commonly used centrality indices (degree, eigenvector, betweenness and closeness) to measure street layout. Due to the presence of spatial dependence, a spatial error model was used to model route choices. Model results demonstrate that: (1) cyclists’ movement models were consistent for the years 2017 and 2018; (2) the presence of a spillover effect suggests that cyclists tend to cycle in proximity to each other; and (3) cyclists avoid streets with high degree centrality values and prefer streets with high eigenvector centrality, betweenness centrality and closeness centrality. These findings reveal cyclists’ desired street layouts and can be taken into consideration for future interventions.

Doctor recommendation technology can help patients filter out large number of irrelevant doctors and find doctors who meet their actual needs quickly and accurately, helping patients gain access to helpful personalized online healthcare services. To address the problems with the existing recommendation methods, this paper proposes a hybrid doctor recommendation model based on online healthcare platform, which utilizes the word2vec model, latent Dirichlet allocation (LDA) topic model, and other methods to find doctors who best suit patients' needs with the information obtained from consultations between doctors and patients. Then, the model treats these doctors as nodes in order to construct a doctor tag cooccurrence network and recommends the most important doctors in the network via an eigenvector centrality calculation model on the graph. This method identifies the important nodes in the entire effective doctor network to support the recommendation from a new graph computing perspective. An experiment conducted on the Chinese healthcare website Chunyuyisheng.com proves that the proposed method a good recommendation performance.

Network scientists have shown that there is great value in studying pairwise interactions between components in a system. From a linear algebra point of view, this involves defining and evaluating functions of the associated adjacency matrix. Recent work indicates that there are further benefits from accounting directly for higher order interactions, notably through a hypergraph representation where an edge may involve multiple nodes. Building on these ideas, we motivate, define and analyze a class of spectral centrality measures for identifying important nodes and hyperedges in hypergraphs, generalizing existing network science concepts. By exploiting the latest developments in nonlinear Perron-Frobenius theory, we show how the resulting constrained nonlinear eigenvalue problems have unique solutions that can be computed efficiently via a nonlinear power method iteration. We illustrate the measures on realistic data sets.

The identification of critical nodes has great practical significance to the urban transportation network (UTN) due to its contribution to enhancing the efficient operation of UTN. Several existing studies have discovered the critical nodes from the perspectives of network topology or passenger flow. However, little attention has been paid to the perspective of service routes in the identification of critical stations, which reflects the closeness of the connection between stations. In order to address the above problem, we propose a two-layer network of UTN to characterize the effects of server routes and present a novel method of critical nodes identification (BMRank). BMRank is inspired by eigenvector centrality, which focuses on network topology and mutual enhancement relationship between stations and server routes, simultaneously. The extensive experiments on the UTN of Shanghai illustrate that BMRank performs better in the identification of critical stations compared with baseline methods. Specifically, the performance of BMRank increases by 12.4% over the best of baseline methods on a low initial failure scale.

We study the 'interconnectedness' of stress-tested banks by exploiting how they are mentioned together in the context of financial news. We start by constructing weekly co-occurrence network matrices following Ronnqvist and Sarlin (2015) text-to-network approach. Using the COVID-19 pandemic as an external shock, we examine how bank networks behave during high stress periods. We find that banks become more interconnected during peaks of COVID-19 induced stress. We put forth a new measure of systemic risk that utilizes text-based eigenvector centrality. This measure provides a more stable ranking system than the traditional SRISK measure during both high and low stress periods.

Like natural complex systems such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time. Thousands of sensors measure relevant process variables at appropriate sampling rates, to provide time series as primary sources for system diagnostics. However, high-dimensionality, non-linearity and non-stationarity of data remain a major challenge to effectively diagnose rare or new system issues by merely using model-based approaches. To reduce the causal search space, we validate an algorithm that applies transfer entropy to obtain a weighted directed graph from a system's multivariate time series and graph eigenvector centrality to identify the system's most influential parameters. The results suggest that this approach robustly identifies the true influential sources in a complex system, even when its information transfer network includes redundant edges., Comment: 5 pages, 4 figures

Under the &ldquo, Internet+&rdquo, environment, the R&amp, D intensity of products and services has increased, hence, organizations need to improve their ability to integrate knowledge and technology resources. Knowledge gaps will arise when an organization&rsquo, s knowledge reserves fail to meet the needs of innovation activities. This research established a network of complete knowledge topics under the &ldquo, environment based on the Word2Vec model. The word vectors and word frequencies of organizational reserve knowledge texts were analyzed to establish an organizational reserve knowledge topic network. The Term Frequency-Inverse Document Frequency algorithm was used to identify the demanded knowledge topic. The satisfaction capability of demanded knowledge in the reserve knowledge topic network was calculated via the eigenvector centrality and the fuzzy evaluation method. The corresponding strategies were then put forward to make up the knowledge gap. Finally, a case study was conducted and compared with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and Venn diagram analysis on the economic and management college of a university in Beijing to verify the effectiveness of this method.

Information flow, opinion, and epidemics spread over structured networks. When using individual node centrality indicators to predict which nodes will be among the top influencers or spreaders in a large network, no single centrality has consistently good ranking power. We show that statistical classifiers using two or more centralities as input are instead consistently predictive over many diverse, static real-world topologies. Certain pairs of centralities cooperate particularly well in statistically drawing the boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood benefit from the addition of a global centrality such as the eigenvector centrality, closeness, or the core number. This is, intuitively, because a local centrality may rank highly some nodes which are located in dense, but peripheral regions of the network---a situation in which an additional global centrality indicator can help by prioritising nodes located more centrally. The nodes selected as superspreaders will usually jointly maximise the values of both centralities. As a result of the interplay between centrality indicators, training classifiers with seven classical indicators leads to a nearly maximum average precision function (0.995) across the networks in this study., 14 pages, 10 figures, 4 supplementary figures

In the network science, there are many network measures and parameters to evaluate the centrality of the nodes in different aspects, such as degree, node betweeenness, closeness, eigenvector centrality, and so on. These measures and parameters can be utilized in crucial nodes determination. In many situations, we also need to evaluate the centrality of the links in the network. However, we only know two measures to describe the centrality of the link: the bridge link and the link betweenness. In this paper, we study the two measures of the links in the network and discover that the centrality of the link betweenness is much more robust and effective than the comprehensive centrality based on the bridge in the random networks, scale-free networks and empirical networks.

The present work is a continuation of the previous study [1] on the performance of short LDPC codes. We have examined a sample of (128,256) different LDPC codes and explored their bit error-rate curves as a function of Tanner graph properties. It was found that certain graph characteristics correlate with LDPC code performance and can help to distinguish LDPC codes with a good decoding threshold (less or equal 3.5 dB) from others. In this work, we investigate a slope of bit error- rate curves that along with a decoding threshold provides an information on code performance.

Graph-based approaches of feature selection have found their application in diverse areas owing to their efficacy to detect potential connections among the features. In this paper, we propose a hybrid graph-based approach for selecting a subset of features in the context of supervised learning. The novelty of this approach lies in the utilisation of graph-theoretic concepts of degree centrality as well as eigenvector centrality to assess the relative importance of features. Additionally, entropy-based measures have been used. The performance of the proposed approach is compared to that of the existing benchmark algorithms using publicly available datasets. The results have come out to be quite encouraging.

Evaluation of the importance of nodes in complex networks has always been the focus of complex network node research, and has practical application value. At present, in the evaluation of node importance, most evaluation indicators and evaluation algorithms are applicable to static networks. For example, based on graph theory and statistical characteristics, node importance evaluation indicators, such as degree and eigenvector centrality, proposed from the perspective of nodes and paths, are applicable to some complex networks, and have certain applicability and limitations. Select multiple evaluation indicators, establish node evaluation index attribute sets, and use multi-attribute decision-making methods, such as principal component analysis, TOPSIS, and attribute reduction sets. In view of the fact that the complex network is constantly changing with time, this paper designs a comprehensive evaluation algorithm for nodes, node attributes and timing based on the TOPSIS algorithm. The Facebook data set for three consecutive months was used, divided into three time periods by month, and the algorithm was verified in chronological order and compared with the results of the TOPSIS algorithm and the PageRank algorithm. Experimental results show that the algorithm takes into account the importance of the nodes in each time period, and the evaluation results are reasonable, more in line with the actual dynamic changes of the nodes, and have higher accuracy.

We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected., 19 pages, 3 figures

We study the blind centrality ranking problem, where our goal is to infer the eigenvector centrality ranking of nodes solely from nodal observations, i.e., without information about the topology of the network. We formalize these nodal observations as graph signals and model them as the outputs of a network process on the underlying (unobserved) network. A simple spectral algorithm is proposed to estimate the leading eigenvector of the associated adjacency matrix, thus serving as a proxy for the centrality ranking. A finite rate performance analysis of the algorithm is provided, where we find a lower bound on the number of graph signals needed to correctly rank (with high probability) two nodes of interest. We then specialize our general analysis for the particular case of dense Erdős-Renyi graphs, where existing graph-theoretical results can be leveraged. Finally, we illustrate the proposed algorithm via numerical experiments on synthetic and real-world networks, with special emphasis on how the network features influence the performance.

In the research community, the estimation of the scholarly impact of an individual is based on either citation-based indicators or network centrality measures. The network-based centrality measures like degree, closeness, betweenness & eigenvector centrality and the citation-based indicators such as h-index, g-index & i10-index, etc., are used and all of the indicators give full credit to all of the authors of a particular article. This is although the contribution of the authors are different. To determine the actual contribution of an author in a particular article, we have applied arithmetic, geometric and harmonic counting methods for finding the actual contribution of an individual. To find the prominent actor in the network, we have applied eigenvector centrality. To authenticate the proposed analysis, an experimental study has been conducted on 186007 authors collaboration network, that is extracted from IEEE Xplore. The experimental results show that the geometric counting-based credit distribution among scholars gives better results than others.

Symmetries are ubiquitous in real networks and often characterize network features and functions. Here we present a generalization of network symmetry called \emph{latent symmetry}, which is an extension of the standard notion of symmetry. They are defined in terms of standard symmetries in a reduced version of the network. One unique aspect of latent symmetries is that each one is associated with a \emph{size}, which provides a way of discussing symmetries at multiple scales in a network. We are able to demonstrate a number of examples of networks (graphs) which contain latent symmetry, including a number of real networks. In numerical experiments, we show that latent symmetries are found more frequently in graphs built using preferential attachment, a standard model of network growth, when compared to non-network like (Erd{\H o}s-R\'enyi) graphs. Finally we prove that if vertices in a network are latently symmetric, then they must have the same eigenvector centrality, similar to vertices which are symmetric in the standard sense. This suggests that the latent symmetries present in real-networks may serve the same structural and functional purpose standard symmetries do in these networks. We conclude from these facts and observations that \emph{latent symmetries} are present in real networks and provide useful information about the network potentially beyond standard symmetries as they can appear at multiple scales., Comment: 18 pages

Knowledge extraction in social networks is a needful tool as it touches every aspect of our lives such as politic, socio-economic, scientific, etc. Community detection is one of the objectives of this specific tool used for knowledge extraction in social networks. Many algorithms of knowledge extraction from social networks have been developed these last years. However, many of them are not constant, effective and accurate when facing these social networks with many edges. In this paper, we propose a new approach of community detection in social networks with many links between communities. The proposed approach has two steps. In the first step, the algorithm attempts to determine all communities that the clique percolation algorithm may find. In the second step, the algorithm computes the Eigenvector Centrality method on the output of the first step in order to measure the influence of network nodes and reduce the rate of the unclassified nodes. To assess this new approach, we test it on different types of networks. Relevant communities that have been detected testifies effectiveness and performance of the approach over other community detection algorithms.

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-the-art algorithms.

Centrality of nodes is very useful for understanding the behavior of systems and has recently attracted plenty of attention from researchers. In this paper, we propose a new eigenvector centrality based on node similarity for ranking nodes in multilayer and temporal networks under the framework of tensor computation, referred to as the ECMSim. We define a fourth-order tensor to represent the multilayer and temporal networks. The relationships between different layers(or time stamps) can be depicted by using node similarity. Based on the defined tensor, we establish the tensor equation to obtain nodes centrality values. The nodes centrality values also can be viewed as the Perron eigenvector of a multi-homogeneous map. Furthermore, we show the existence and uniqueness of the proposed centrality measure by existing results. Numerical experiments are carried out to demonstrate that the proposed centrality outperforms some existing ranking methods.

The strength of information diffusion on social networks depends on many factors, including the selected influential nodes. The problem of finding such nodes in the network is modeled by influence maximization problem, which faces two essential challenges: (1) inadequate selection of the seed nodes due to the lack of focus on the rich-club phenomenon and (2) high running time due to the lack of focus on pruning the graph nodes and localization. To solve these challenges, a computational localization-based RLIM algorithm is presented here to prevent the rich-club phenomenon. In this algorithm, the graph nodes are pruned based on the eigenvector centrality to reduce the computational overhead, and then the computations are performed locally using localization criteria. After that, influential nodes are selected by avoiding the rich-club phenomenon. In the RLIM algorithm, the seed nodes provided a better influence spread than the other algorithms. Experimental results on the synthetic and real-world datasets shows that the RLIM algorithm can verify the high effectiveness and efficiency than the comparable algorithms for an influence maximization problem.

Many of the structural characteristics of a network depend on the connectivity with and within the hubs. These dependencies can be related to the degree of a node and the number of links that a node shares with nodes of higher degree. In here we revise and present new results showing how to construct network ensembles which give a good approximation to the degree–degree correlations, and hence to the projections of this correlation like the assortativity coefficient or the average neighbours degree. We present a new bound for the structural cut–off degree based on the connectivity within the hubs. Also we show that the connections with and within the hubs can be used to define different networks cores. Two of these cores are related to the spectral properties and walks of length one and two which contain at least on hub node, and they are related to the eigenvector centrality. We introduce a new centrality measured based on the connectivity with the hubs. In addition, as the ensembles and cores are related by the connectivity of the hubs, we show several examples how changes in the hubs linkage effects the degree–degree correlations and core properties.

The detection of the nodes that are more influencing in a complex network is considered as a critical research aspect. There are many intuitive thoughts about what a center node measure. The greater a node is centred, the greater it becomes crucial. Formally, a centrality degree is a real-valued function at the node of the graph. In this paper, we present, the existing research works on the measures of centrality in complicated networks and advise the exceptional centrality degree for a complex community. By finding the most influential node in a complex network, we can spread the message in an efficient and faster way. Our proposed work illustrates the method to get the most dominant nodes in a complex network.

Identifying the influential nodes in temporal networks has attracted lots of attention recently. In this paper, we present an Improved Eigenvector-based Centrality Measures (IECM) for temporal networks by regarding the coupling strength between proximity layers as the inter-layer similarity. Compared with the results of the nodes’ influences got by temporal global efficiency for two real networks, the IECM method could identify influential nodes more accurately than the traditional ECM method. Regarding to the fact that different kinds of measurements have different performances, we introduce the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to measure the global performance. Specially, when the inter-layer coupling strength ω set as 1 in the ECM method, the accuracy could be averagely enhanced 18.75% and 29.65% at each time layer for Workspace and Enrons datasets respectively, which indicates that measuring the inter-layer coupling strength plays an important role for identifying the influential nodes.

Performance analysis in volleyball has seldom analysed the interrelationships of game actions under the systemic view of distinct game complexes, and how different patterns of game flow emerge. In this study, we used Social Network Analysis with eigenvector centrality to weight direct and indirect relationships between game actions and to assess the similarities and differences between six game complexes in high-level men’s volleyball. The study sample comprised 10 matches of the final phase of the 2015 World League (1600 game actions). Results indicated that dividing the game into six complexes and analysing game actions as nodes offers a more detailed understanding of the game and highlights the distinct constraints that typify each game complex. Specifically, the use of eigenvector centrality afforded a more accurate weighting of the variables for each complex. Because off-system situations were predominant in several game complexes, i.e.: Setting Condition C in Complex I (0.36), Complex II (0....

One of the most interesting challenges in network science is to understand the relation between network structure and dynamics on it, and many topological properties, including degree distribution, community strength and clustering coefficient, have been proposed in the last decade. Prominent in this context is the centrality measures, which aim at quantifying the relative importance of individual nodes in the overall topology with regard to network organization and function. However, most of the previous centrality measures have been proposed based on different concepts and each of them focuses on a specific structural feature of networks. Thus, the straightforward and standard methods may lead to some bias against node importance measure. In this paper, we introduce two physical processes with potential complementarity between them. Then we propose to combine them as an elegant integration with the classic eigenvector centrality framework to improve the accuracy of node ranking. To test the produced power iteration ranking (PIRank) algorithm, we apply it to the selection of attack targets in network optimal attack problem. Extensive experimental results on synthetic networks and real-world networks suggest that the proposed centrality performs better than other well-known measures. Moreover, comparing with the eigenvector centrality, the PIRank algorithm can achieve about thirty percent performance improvement while keeping similar running time. Our experiment on random networks also shows that PIRank algorithm can avoid the localization phenomenon of eigenvector centrality, in particular for the networks with high-degree hubs.

In complex networks, different nodes have distinct impact on overall functionality and resiliency against failures. Hence, identifying vital nodes is crucial to limit the size of the damage during a cascade failure process, enabling us to identify the most vulnerable nodes and to take solid protection measures to deter them from failure. In this manuscript, we study the correlation between cascade depth, i.e. the number of failed nodes as a consequence of single failure in one of the nodes, and centrality measures including degree, betweenness, closeness, clustering coefficient, local rank, eigenvector centrality, lobby index and information index. Networks behave dissimilarly against cascade failure due to their different structures. Interestingly, we find that node degree is negatively correlated with the cascade depth, meaning that failing a high-degree node has less severe effect than the case when lower-degree nodes fail. Betweenness centrality and local rank show positive correlation with the cascade depth. In order to make networks more resilient against cascade failures, one can remove nodes that ranked high in terms of those centrality measures showing negative correlation with the cascade depth.

Modeling intents are what engineers wish the part to be (even under changes) modeled in order to reflect his original design considerations, dependencies and constraints. Such intents in the current Computer-aided Design (CAD) modeling practice are not systematically captured, and often represented in the form of implicit constraints embedded in features. In order to unveil modeling intents it is necessary to analyze the feature relations. Feature dependency graph for a part model is created by extracting historical modeling operations and the dependency information of each feature. It offers a more organized view toward the model construction. A posterior analysis of CAD models is proposed to unveil modeling intents by examining feature dependencies with different graph measures, including degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. This paper shows critical feature for the construction of the CAD model can be identified with the centrality analysi...

Small and medium-sized ports (SMPs) have been challenged by inadequate capacity and trade volume due to the worldwide economic boom. One of the strategies used to solve this problem is creating new routes with applicable nearby ports. However, the correct and influential ports to connect with must be identified. Hence, we used Social Network Analysis (SNA), including degree centrality, betweenness centrality, and eigenvector centrality, to analyze the maritime container transportation route network of the Port of Incheon in South Korea. Five ports were finalized as candidates after combining the SNA method, local political policies, and the port's current connection status. Keywords: Eigenvector Centrality, Incheon Port, Small and Midium-sized Port, Selecting Ports, Social Network Analysis, Port Centrality

In recent years, epidemics have been raging around the world, causing serious harm to human health, and controlling the spread of the epidemics has become a hot topic. The study of network partition is of great practical significance to control the spread of the epidemics. Network partition is an N P hard problem. Much work has focused on identifying important nodes in complex networks to achieve network partition. Similarly, understanding the importance of links in spreading dynamics in a network can provide ways to hinder or slow down ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. This paper studies the algorithms to identify influential links in complex networks. Deleting some influential links can divide the network into some components so as to prevent effectively the spreading processes in epidemics. Our algorithms can successfully identify influential links at low computational cost by utilizing the eigenvector centrality measures. The simulated results in some real-world social networks and synthetic networks show that our algorithms are effective. The number of infected individuals can be remarkable reduced by removing influential links.

We build a complex networks model of combat System-of-Systems (SoS) based on empirical data from a real war-game, this model is a combination of command & control (C2) subnetwork, sensors subnetwork, influencers subnetwork and logistical support subnetwork, each subnetwork has idiographic components and statistical characteristics. The C2 subnetwork is the core of whole combat SoS, it has a hierarchical structure with no modularity, of which robustness is strong enough to maintain normal operation after any two nodes is destroyed; the sensors subnetwork and influencers subnetwork are like sense organ and limbs of whole combat SoS, they are both flat modular networks of which degree distribution obey GEV distribution and power-law distribution respectively. The communication network is the combination of all subnetworks, it is an assortative Small-World network with core–periphery structure, the Intelligence & Communication Stations/Command Center integrated with C2 nodes in the first three level act as the hub nodes in communication network, and all the fourth-level C2 nodes, sensors, influencers and logistical support nodes have communication capability, they act as the periphery nodes in communication network, its degree distribution obeys exponential distribution in the beginning, Gaussian distribution in the middle, and power-law distribution in the end, and its path length obeys GEV distribution. The betweenness centrality distribution, closeness centrality distribution and eigenvector centrality are also been analyzed to measure the vulnerability of nodes.

In human resource management, employee performance is strongly affected by both formal and informal employee networks. Most previous research on employee performance has focused on monolayer networks that can represent only single categories of employee social relationships. We study employee performance by taking into account the entire multiplex structure of underlying employee social networks. We collect three datasets consisting of five different employee relationship categories in three firms, and predict employee performance using degree centrality and eigenvector centrality in a superimposed multiplex network (SMN) and an unfolded multiplex network (UMN). We use a quadratic assignment procedure (QAP) analysis and a regression analysis to demonstrate that the different categories of relationship are mutually embedded and that the strength of their impact on employee performance differs. We also use weighted/unweighted SMN/UMN to measure the predictive accuracy of this approach and find that employees with high centrality in a weighted UMN are more likely to perform well. Our results shed new light on how social structures affect employee performance.

In this work we look for characteristics and mobility patterns in the cities of Rome and London, from a dataset of private vehicle movements in those cities. Based on mobility data and other data related to the urban public transport network, commercial activity and tourist information, a multiplex network with three layers is constructed for each city. The construction of the multiplex network allows us to establish relationships between mobility and urban bus transport system with tourism and commercial activities. From these networks, two measures of centrality in multiplex networks are calculated based on the spectral properties of a matrix constructed from the network graph and the data associated with the nodes. The centrality measures establish a ranking in the importance of the nodes within the graph. This allows us to identify the most important zones or areas within the urban layout, both from the point of view of mobility and displacement and of tourist and leisure activity within the city. Centrality mapping helps us to establish different characteristics and patterns in the car displacements in both cities. This work is supported by the Spanish Government, Ministerio de Economía y Competividad, grant number TIN2017-84821-P.

The timely and efficient cooperative distribution of a streamlined content in a communication network is a key feature for many applications and services. One of the unsolved problems is the assignment of transmission rates to nodes, given the constraints imposed by the topology, so that all nodes receive the stream with the minimal global use of resources. This letter addresses the problem exploiting the notion of eigenvector centrality. It shows that the problem can be solved efficiently in a distributed way if every node is aware of the full network topology and that in certain cases only local information on the network graph is sufficient.

We present an application of eigenvector centrality to encode the connectivity of polymer networks resolved at the micro- and meso-scopic length scales. This method captures the relative importance of different nodes within the network structure and provides a route toward the development of a statistical mechanics model that correlates connectivity with mechanical response. This scheme may be informed by analytical and semi-analytical models for the network structure, or through direct experimental examination. It may be used to predict the reduction in mechanical performance for heterogeneous materials subjected to specific modes of damage. Here, we develop the method and demonstrate that it leads to the prediction of established trends in elastomers. We also apply the model to the case of a self-healing polymer network reported in the literature, extracting insight about the fraction of bonds broken and re-formed during strain and recovery.