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Showing papers on "Betweenness centrality published in 2012"


Journal ArticleDOI
TL;DR: Simulations on four real networks show that the proposed semi-local centrality measure can well identify influential nodes and is a tradeoff between the low-relevant degree centrality and other time-consuming measures.
Abstract: Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.

898 citations


Book ChapterDOI
21 May 2012
TL;DR: A centrality-based caching algorithm is proposed by exploiting the concept of (ego network) betweenness centrality to improve the caching gain and eliminate the uncertainty in the performance of the simplistic random caching strategy.
Abstract: Ubiquitous in-network caching is one of the key aspects of information-centric networking (ICN) which has recently received widespread research interest. In one of the key relevant proposals known as Networking Named Content (NNC), the premise is that leveraging in-network caching to store content in every node it traverses along the delivery path can enhance content delivery. We question such indiscriminate universal caching strategy and investigate whether caching less can actually achieve more . Specifically, we investigate if caching only in a subset of node(s) along the content delivery path can achieve better performance in terms of cache and server hit rates. In this paper, we first study the behavior of NNC's ubiquitous caching and observe that even naive random caching at one intermediate node within the delivery path can achieve similar and, under certain conditions, even better caching gain. We propose a centrality-based caching algorithm by exploiting the concept of (ego network) betweenness centrality to improve the caching gain and eliminate the uncertainty in the performance of the simplistic random caching strategy. Our results suggest that our solution can consistently achieve better gain across both synthetic and real network topologies that have different structural properties.

360 citations


Journal ArticleDOI
TL;DR: A complete database for the scientific specialty of research about “steel structures” shows that betweenness centrality of an existing node is a significantly better predictor of preferential attachment by new entrants than degree or closeness centrality.

320 citations


Journal ArticleDOI
27 Sep 2012-PLOS ONE
TL;DR: In this paper, the authors introduced the concept of control centrality to quantify the ability of a single node to control a directed weighted network and showed that it is mainly determined by the network's degree distribution.
Abstract: We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.

292 citations


Journal ArticleDOI
TL;DR: A simple but powerful model, the time-ordered graph, is presented, which reduces a dynamic network to a static network with directed flows, which enables it to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case.
Abstract: Many networks are dynamic in that their topology changes rapidly---on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

276 citations


Proceedings ArticleDOI
25 Mar 2012
TL;DR: This work considers several graph-related centrality metrics to allocate content store space heterogeneously across the Content Centric Networking network, and contrasts the performance to that of an homogeneous allocation.
Abstract: In this work, we study the caching performance of Content Centric Networking (CCN), with special emphasis on the size of individual CCN router caches. Specifically, we consider several graph-related centrality metrics (e.g., betweenness, closeness, stress, graph, eccentricity and degree centralities) to allocate content store space heterogeneously across the CCN network, and contrast the performance to that of an homogeneous allocation.

240 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examined the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain and found that the correlation is higher with secondary than primary activities.
Abstract: The paper examines the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain. The focus is on what type of street centrality (closeness, betweenness and straightness) is more closely associated with which type of economic activity (primary and secondary). Centralities are calculated purely on the street network by using a multiple centrality assessment model, and a kernel density estimation method is applied to both street centralities and economic activities to permit correlation analysis between them. Results indicate that street centralities are correlated with the location of economic activities and that the correlations are higher with secondary than primary activities. The research suggests that, in urban planning, central urban arterials should be conceived as the cores, not the borders, of neighbourhoods.

239 citations


Journal ArticleDOI
TL;DR: The integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins, and the proposed new centrality measure PeC is an effective essential protein discovery method.
Abstract: Background: Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins’ essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value. Results: In this paper, we propose a new centrality measure, named PeC, based on the integration of proteinprotein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of Saccharomyces cerevisiae. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized a-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins. Conclusions: We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.

209 citations


Journal ArticleDOI
TL;DR: This work applied 3 contrasting linkage-mapping methods to spatial data representing wolf habitat to analyze connectivity between wolf populations in central Idaho and Yellowstone National Park, and identified diffuse networks that included alternative linkages that will allow greater flexibility in planning.
Abstract: Centrality metrics evaluate paths between all possible pairwise combinations of sites on a landscape to rank the contribution of each site to facilitating ecological flows across the network of sites. Computational advances now allow application of centrality metrics to landscapes represented as continuous gradients of habitat quality. This avoids the binary classification of landscapes into patch and matrix required by patch-based graph analyses of connectivity. It also avoids the focus on delineating paths between individual pairs of core areas characteristic of most corridor- or linkage-mapping methods of connectivity analysis. Conservation of regional habitat connectivity has the potential to facilitate recovery of the gray wolf (Canis lupus), a species currently recolonizing portions of its historic range in the western United States. We applied 3 contrasting linkage-mapping methods (shortest path, current flow, and minimum-cost-maximum-flow) to spatial data representing wolf habitat to analyze connectivity between wolf populations in central Idaho and Yellowstone National Park (Wyoming). We then applied 3 analogous betweenness centrality metrics to analyze connectivity of wolf habitat throughout the northwestern United States and southwestern Canada to determine where it might be possible to facilitate range expansion and interpopulation dispersal. We developed software to facilitate application of centrality metrics. Shortest-path betweenness centrality identified a minimal network of linkages analogous to those identified by least-cost-path corridor mapping. Current flow and minimum-cost-maximum-flow betweenness centrality identified diffuse networks that included alternative linkages, which will allow greater flexibility in planning. Minimum-cost-maximum-flow betweenness centrality, by integrating both land cost and habitat capacity, allows connectivity to be considered within planning processes that seek to maximize species protection at minimum cost. Centrality analysis is relevant to conservation and landscape genetics at a range of spatial extents, but it may be most broadly applicable within single- and multispecies planning efforts to conserve regional habitat connectivity.

186 citations


Journal ArticleDOI
TL;DR: The validness and robustness of this new centrality measure is investigated by illustrating this method to some classical weighted social network data sets and obtaining reliable results, which provide strong evidences of the new measure's utility.

157 citations


Journal ArticleDOI
TL;DR: Results suggest that research performance of scholars' is significantly correlated with scholars' ego-network measures, and scholars with efficient collaboration networks who maintain a strong co-authorship relationship with one primary co-author within a group of linked co-authors perform better than those researchers with many relationships to the same group of links.
Abstract: In this study, we propose and validate social networks based theoretical model for exploring scholars' collaboration (co-authorship) network properties associated with their citation-based research performance (i.e., g-index). Using structural holes theory, we focus on how a scholar's egocentric network properties of density, efficiency and constraint within the network associate with their scholarly performance. For our analysis, we use publication data of high impact factor journals in the field of ''Information Science & Library Science'' between 2000 and 2009, extracted from Scopus. The resulting database contained 4837 publications reflecting the contributions of 8069 authors. Results from our data analysis suggest that research performance of scholars' is significantly correlated with scholars' ego-network measures. In particular, scholars with more co-authors and those who exhibit higher levels of betweenness centrality (i.e., the extent to which a co-author is between another pair of co-authors) perform better in terms of research (i.e., higher g-index). Furthermore, scholars with efficient collaboration networks who maintain a strong co-authorship relationship with one primary co-author within a group of linked co-authors (i.e., co-authors that have joint publications) perform better than those researchers with many relationships to the same group of linked co-authors.

Journal ArticleDOI
31 May 2012-PLOS ONE
TL;DR: It is shown that high values of node betweenness and vulnerability correlate well with recorded large food poisoning outbreaks, and the IFTN provides a vehicle suitable for the fast distribution of potential contaminants but unsuitable for tracing their origin.
Abstract: With the world’s population now in excess of 7 billion, it is vital to ensure the chemical and microbiological safety of our food, while maintaining the sustainability of its production, distribution and trade. Using UN databases, here we show that the international agro-food trade network (IFTN), with nodes and edges representing countries and import-export fluxes, respectively, has evolved into a highly heterogeneous, complex supply-chain network. Seven countries form the core of the IFTN, with high values of betweenness centrality and each trading with over 77% of all the countries in the world. Graph theoretical analysis and a dynamic food flux model show that the IFTN provides a vehicle suitable for the fast distribution of potential contaminants but unsuitable for tracing their origin. In particular, we show that high values of node betweenness and vulnerability correlate well with recorded large food poisoning outbreaks.

Journal ArticleDOI
06 Jul 2012-PLOS ONE
TL;DR: Applying the notion of betweenness centrality to 28 worldwide metro systems is applied to study the emergence of global trends in the evolution of centrality with network size and offers significant insights that can help planners in their task to design the systems of tomorrow.
Abstract: Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need This paper examines one fundamental aspect of transit: network centrality By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail Betweenness was notably found to consistently become more evenly distributed with size (ie no “winner takes all”) unlike other complex network properties Two distinct regimes were also observed that are representative of their structure Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (087 vs 248) Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (eg, electricity grid, water/wastewater system, etc) to develop more sustainable cities

Journal ArticleDOI
TL;DR: The results show that the extended betweenness is superior to topological betweenness in the identification of critical components in power grids and at the same time could be a complementary tool to efficiently enhance vulnerability analysis based on electrical engineering methods.
Abstract: Vulnerability analysis in power systems is a key issue in modern society and many efforts have contributed to the analysis. Recently, complex networks metrics, applied to assess the topological vulnerability of networked systems, have been used in power grids, such as the betweenness centrality. These metrics may be useful for analyzing the topological vulnerability of power systems because of a close link between their topological structure and physical behavior. However, a pure topological approach fails to capture the electrical specificity of power grids. For this reason, an extended topological method has been proposed by incorporating several electrical features, such as electrical distance, power transfer distribution, and line flow limits, into the pure topological metrics. Starting from the purely topological concept of complex networks, this paper defines an extended betweenness centrality which considers the characteristics of power grids and can measure the local importance of the elements in power grids. The line extended betweenness is compared with the topological betweenness and with the averaged power flow on each line over various operational states in the Italian power grid. The results show that the extended betweenness is superior to topological betweenness in the identification of critical components in power grids and at the same time could be a complementary tool to efficiently enhance vulnerability analysis based on electrical engineering methods.

Journal ArticleDOI
TL;DR: This work proposes an efficient algorithm, running in O(@km), being m the number of edges in the graph, that is feasible for large scale network analysis and defines the @k-path edge centrality, a measure of centrality introduced to compute the importance of edges.
Abstract: The problem of assigning centrality values to nodes and edges in graphs has been widely investigated during last years. Recently, a novel measure of node centrality has been proposed, called @k-path centrality index, which is based on the propagation of messages inside a network along paths consisting of at most @k edges. On the other hand, the importance of computing the centrality of edges has been put into evidence since 1970s by Anthonisse and, subsequently by Girvan and Newman. In this work we propose the generalization of the concept of @k-path centrality by defining the @k-path edge centrality, a measure of centrality introduced to compute the importance of edges. We provide an efficient algorithm, running in O(@km), being m the number of edges in the graph. Thus, our technique is feasible for large scale network analysis. Finally, the performance of our algorithm is analyzed, discussing the results obtained against large online social network datasets.

Journal ArticleDOI
TL;DR: The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics and stability against thresholding for global efficiency, clustering coefficient and diversity.

Journal ArticleDOI
TL;DR: EBC is based on the concept of betweenness centrality, which has been first introduced in the context of social network analysis, and measures the ''importance'' of each node in the network, and outperforms the competitor ones in all observed cases.

Proceedings ArticleDOI
Min-Joong Lee1, Jung Min Lee1, Jaimie Yejean Park1, Ryan Hyun Choi1, Chin-Wan Chung1 
16 Apr 2012
TL;DR: This work proposes a method that efficiently reduces the search space by finding a candidate set of vertices whose betweenness centralities can be updated and computes their betweenness centeralities using candidate vertices only.
Abstract: The betweenness centrality of a vertex in a graph is a measure for the participation of the vertex in the shortest paths in the graph. The Betweenness centrality is widely used in network analyses. Especially in a social network, the recursive computation of the betweenness centralities of vertices is performed for the community detection and finding the influential user in the network. Since a social network graph is frequently updated, it is necessary to update the betweenness centrality efficiently. When a graph is changed, the betweenness centralities of all the vertices should be recomputed from scratch using all the vertices in the graph. To the best of our knowledge, this is the first work that proposes an efficient algorithm which handles the update of the betweenness centralities of vertices in a graph. In this paper, we propose a method that efficiently reduces the search space by finding a candidate set of vertices whose betweenness centralities can be updated and computes their betweenness centeralities using candidate vertices only. As the cost of calculating the betweenness centrality mainly depends on the number of vertices to be considered, the proposed algorithm significantly reduces the cost of calculation. The proposed algorithm allows the transformation of an existing algorithm which does not consider the graph update. Experimental results on large real datasets show that the proposed algorithm speeds up the existing algorithm 2 to 2418 times depending on the dataset.

Posted Content
TL;DR: In this paper, the authors developed a theoretical model showing who the key player is, i.e., the criminal who once removed generates the highest possible reduction in aggregate crime level.
Abstract: We analyze delinquent networks of adolescents in the United States. We develop a theoretical model showing who the key player is, i.e. the criminal who once removed generates the highest possible reduction in aggregate crime level. We also show that key players are not necessary the most active criminals in a network. We then test our model using data on criminal behaviors of adolescents in the United States (AddHealth data). Compared to other criminals, key players are more likely to be a male, have less educated parents, are less attached to religion and feel socially more excluded. They also feel that adults care less about them, are less attached to their school and have more troubles getting along with the teachers. We also find that, even though some criminals are not very active in criminal activities, they can be key players because they have a crucial position in the network in terms of betweenness centrality.

Journal ArticleDOI
TL;DR: This research examines the roles that the social network structures of employees, and the organizational units where they work, play in influencing the postimplementation success of enterprise systems and finds that centralized structures inhibit implementation success.
Abstract: The implementation of enterprise systems has yielded mixed and unpredictable outcomes in organizations. Although the focus of prior research has been on training and individual self-efficacy as important enablers, we examine the roles that the social network structures of employees, and the organizational units where they work, play in influencing the postimplementation success. Data were gathered across several units within a large organization: immediately after the implementation, six months after the implementation, and one year after the implementation. Social network analysis was used to understand the effects of network structures, and hierarchical linear modeling was used to capture the multilevel effects at unit and individual levels. At the unit level of analysis, we found that centralized structures inhibit implementation success. At the individual level of analysis, employees with high in-degree and betweenness centrality reported high task impact and information quality. We also found a cross...

Journal ArticleDOI
TL;DR: In this paper, the link between household resilience and connectivity in a rural community in Botswana was examined, and the authors found that households with greater social connectivity have greater resilience, and analyzed a community in rural Botswana to uncover how different households make use of social networks to deal with shocks such as human illness and death, crop damage, and livestock disease.
Abstract: Adaptability is emerging as a key issue not only in the climate change debate but in the general area of sustainable development. In this context, we examine the link between household resilience and connectivity in a rural community in Botswana. We see resilience and vulnerability as the positive and negative dimensions of adaptability. Poor, marginal rural communities confronted with the vagaries of climate change, will need to become more resilient if they are to survive and thrive. We define resilience as the capacity of a social-ecological system to cope with shocks such as droughts or economic crises without changing its fundamental identity. We make use of three different indices of household resilience: livelihood diversity, wealth, and a comprehensive resilience index based on a combination of human, financial, physical, social, and natural capital. Then, we measure the social connectivity of households through a whole network approach in social network analysis, using two measures of network centrality (degree centrality and betweenness). We hypothesize that households with greater social connectivity have greater resilience, and analyze a community in rural Botswana to uncover how different households make use of social networks to deal with shocks such as human illness and death, crop damage, and livestock disease. We surveyed the entire community of Habu using a structured questionnaire that focused on livelihood strategies and social networks. We found that gender, age of household head, and household size were positively correlated with social connectivity. Our analysis indicates that those households that are more socially networked are likely to have a wider range of livelihood strategies, greater levels of other forms of social capital, and greater overall capital. Therefore, they are more resilient.

Journal ArticleDOI
TL;DR: The results indicate that papers tend to be cited in each research field locally, and one must consider the typology of targeted research areas when building models for link prediction in citation networks.
Abstract: In this article, we build models to predict the existence of citations among papers by formulating link prediction for 5 large-scale datasets of citation networks. The supervised machine-learning model is applied with 11 features. As a result, our learner performs very well, with the F1 values of between 0.74 and 0.82. Three features in particular, link-based Jaccard coefficient, difference in betweenness centrality, and cosine similarity of term frequency-inverse document frequency vectors, largely affect the predictions of citations. The results also indicate that different models are required for different types of research areas--research fields with a single issue or research fields with multiple issues. In the case of research fields with multiple issues, there are barriers among research fields because our results indicate that papers tend to be cited in each research field locally. Therefore, one must consider the typology of targeted research areas when building models for link prediction in citation networks.

Journal ArticleDOI
TL;DR: It is found that node importance is highly predictable due to both periodic and legacy effects of human social behaviour, and reasonable prediction functions are designed that can be efficiently computed in linear time, and are thus practical for processing dynamic networks in real-time.

Journal ArticleDOI
TL;DR: It is shown that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links.
Abstract: Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes.

Journal ArticleDOI
01 Jul 2012-Animal
TL;DR: The French swine trade network appeared less structurally vulnerable than ruminant trade networks, but inside communities, the hierarchical structure of the swine production system would favour the spread of an infectious agent (especially if introduced in breeding herds).
Abstract: The networks generated by live animal movements are the principal vector for the propagation of infectious agents between farms, and their topology strongly affects how fast a disease may spread. The structural characteristics of networks may thus provide indicators of network vulnerability to the spread of infectious disease. This study applied social network analysis methods to describe the French swine trade network. Initial analysis involved calculating several parameters to characterize networks and then identifying high-risk subgroups of holdings for different time scales. Holding-specific centrality measurements ('degree', 'betweenness' and 'ingoing infection chain'), which summarize the place and the role of holdings in the network, were compared according to the production type. In addition, network components and communities, areas where connectedness is particularly high and could influence the speed and the extent of a disease, were identified and analysed. Dealer holdings stood out because of their high centrality values suggesting that these holdings may control the flow of animals in part of the network. Herds with growing units had higher values for degree and betweenness centrality, representing central positions for both spreading and receiving disease, whereas herds with finishing units had higher values for in-degree and ingoing infection chain centrality values and appeared more vulnerable with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases. This reflects the dynamics of the swine trade with downward movements along the production chain. But, the significant heterogeneity of farms with several production units did not reveal any particular type of production for targeting disease surveillance or control. Besides, no giant strong connected component was observed, the network being rather organized according to communities of small or medium size (<20% of network size). Because of this fragmentation, the swine trade network appeared less structurally vulnerable than ruminant trade networks. This fragmentation is explained by the hierarchical structure, which thus limits the structural vulnerability of the global trade network. However, inside communities, the hierarchical structure of the swine production system would favour the spread of an infectious agent (especially if introduced in breeding herds).

Journal ArticleDOI
27 Jul 2012-PLOS ONE
TL;DR: This study focuses on the identification of controlling regions in cortical networks of cats’ brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods and shows that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes.
Abstract: Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats’ brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats’ brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.

Journal ArticleDOI
TL;DR: It is found that the centrality of a plant was related to its fitness, with plants occupying central positions having higher fitness than those occupying peripheral positions in networks, in relation to the distribution of conspecific phenotypes.
Abstract: The relationships among the members of a population can be visualized using individual networks, where each individual is a node connected to each other by means of links describing the interactions. The centrality of a given node captures its importance within the network. We hypothesize that in mutualistic networks, the centrality of a node should benefit its fitness. We test this idea studying eight individual-based networks originated from the interaction between Erysimum mediohispanicum and its flower visitors. In these networks, each plant was considered a node and was connected to conspecifics sharing flower visitors. Centrality indicates how well connected is a given E. mediohispanicum individual with the rest of the co-occurring conspecifics because of sharing flower visitors. The centrality was estimated by three network metrics: betweenness, closeness and degree. The complex relationship between centrality, phenotype and fitness was explored by structural equation modelling. We found that the centrality of a plant was related to its fitness, with plants occupying central positions having higher fitness than those occupying peripheral positions. The structural equation models (SEMs) indicated that the centrality effect on fitness was not merely an effect of the abundance of visits and the species richness of visitors. Centrality has an effect even when simultaneously accounting for these predictors. The SEMs also indicated that the centrality effect on fitness was because of the specific phenotype of each plant, with attractive plants occupying central positions in networks, in relation to the distribution of conspecific phenotypes. This finding suggests that centrality, owing to its dependence on social interactions, may be an appropriate surrogate for the interacting phenotype of individuals.

Posted ContentDOI
TL;DR: In this article, the authors developed a dynamic network formation model showing who the key player is, i.e., the criminal who once removed generates the highest possible reduction in aggregate crime level.
Abstract: We analyze delinquent networks of adolescents in the United States. We develop a dynamic network formation model showing who the key player is, i.e. the criminal who once removed generates the highest possible reduction in aggregate crime level. We then structurally estimate our model using data on criminal behaviors of adolescents in the United States (AddHealth data). Compared to other criminals, key players are more likely to be male, have less educated parents, are less attached to religion and feel socially more excluded. We also find that, even though some criminals are not very active in criminal activities, they can be key players because they have a crucial position in the network in terms of betweenness centrality.

Proceedings ArticleDOI
16 Apr 2012
TL;DR: It is shown that k-centrality measures are good approximations for the corresponding centrality measures by achieving a tremendous gain of calculation time and also having linear calculation complexity O(n) for networks with constant average degree.
Abstract: A lot of centrality measures have been developed to analyze different aspects of importance. Some of the most popular centrality measures (e.g. betweenness centrality, closeness centrality) are based on the calculation of shortest paths. This characteristic limits the applicability of these measures for larger networks. In this article we elaborate on the idea of bounded-distance shortest paths calculations. We claim criteria for k-centrality measures and we introduce one algorithm for calculating both betweenness and closeness based centralities. We also present normalizations for these measures. We show that k-centrality measures are good approximations for the corresponding centrality measures by achieving a tremendous gain of calculation time and also having linear calculation complexity O(n) for networks with constant average degree. This allows researchers to approximate centrality measures based on shortest paths for networks with millions of nodes or with high frequency in dynamically changing networks.

Journal ArticleDOI
TL;DR: Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables, and contextual network based variables turn out to have the highest impact on discriminating churners from non-churners.
Abstract: This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable.