Abstract: Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.

Abstract: This article seeks to demonstrate the centrality of maintenance and repair to an understanding of modern societies and, particularly, cities. Arguing that repair and maintenance activities present ...

Abstract: In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004 (Thomson ISI, Philadelphia, PA), and then also in relation to local citation environments that can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization; otherwise, the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator's local character. Maps showing interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments, which is available online.

Abstract: We introduce delta centralities, a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality C I , which is based on the concept of efficient propagation of information over the network. C I is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world.

Abstract: A survey of the use of graph theoretical techniques in Biology is presented. In particular, recent work on identifying and modelling the structure of bio-molecular networks is discussed, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronisation and disease propagation.

Abstract: Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.

Abstract: Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n2 log n) time for weighted graphs, where n is the number of vertices and m is the number of edges in the network. These are also the worstcase time bounds for computing the betweenness score of a single vertex. In this paper, we present a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs. Our approximation algorithm is based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality. We conduct an extensive experimental study on real-world graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biological networks, road networks and web crawls.

Abstract: In the U.S. House and Senate, each piece of legislation is sponsored by a unique legislator. In addition, legislators can publicly express support for a piece of legislation by cosponsoring it. The network of sponsors and cosponsors provides information about the underlying social networks among legislators. I use a number of statistics to describe the cosponsorship network in order to show that it behaves much differently than other large social networks that have been recently studied. In particular, the cosponsorship network is much denser than other networks and aggregate features of the network appear to be influenced by institutional arrangements and strategic incentives. I also demonstrate that a weighted closeness centrality measure that I call 'connectedness' can be used to identify influential legislators.

Abstract: Using a sample of 89 mid-level managers in a US based urban hospital, this study investigates relationships among three measures of network centrality and managers' divergent strategic activity. While prior work has demonstrated a relationship between managers' boundary-spanning responsibilities and strategic activity, inadequate attention has been paid to managers' internal network position. Drawing from established theory, we consider expected network flows associated with three elements of the strategic renewal process. From this, we hypothesize and test relationships among managers' divergent activity and three measures of network centrality. Our findings suggest specific relationships between alternative forms of network centrality and particular elements of the strategic renewal process. Consistent with existing research, the findings also show boundary-spanning managers to be more strategically active than their non-boundary-spanning counterparts.

Abstract: The purposes of this study were to determine whether fashion consumer groups (fashion followers, fashion innovators, fashion opinion leaders and innovative communicators) differ in centrality of visual product aesthetics, consumers' need for uniqueness and need for touch, and to examine possible correlations among these variables. Fashion design and merchandizing students completed the following scales: Need for Touch, Consumers' Need for Uniqueness, Centrality of Visual Product Aesthetics, Measure of Fashion Innovativeness and Opinion Leadership plus demographic information. Innovative communicators had a greater need for uniqueness than followers and opinion leaders, but not than innovators. Fashion followers scored lower on Centrality of Visual Product Aesthetics than did innovative communicators, innovators and opinion leaders. There was no difference in Need for Touch among fashion consumer groups. Scores on Need for Touch were positively correlated with scores on Centrality of Visual Product Aesthetics and Consumers' Need for Uniqueness. Scores on Centrality of Visual Product Aesthetics were positively correlated with scores on Consumers' Need for Uniqueness. Scores on Fashion Innovativeness and Opinion Leadership were positively correlated with scores on Centrality of Visual Product Aesthetics and Consumers' Need for Uniqueness. Scores on Fashion Innovativeness and Opinion Leadership were not significantly correlated with those on Need for Touch. Fashion consumer groups did differ in centrality of visual product aesthetics and need for uniqueness, but not in need for touch.

Abstract: Transport in weighted networks is dominated by the minimum spanning tree (MST), the tree connecting all nodes with the minimum total weight. We find that the MST can be partitioned into two distinct components, having significantly different transport properties, characterized by centrality--the number of times a node (or link) is used by transport paths. One component, superhighways, is the infinite incipient percolation cluster, for which we find that nodes (or links) with high centrality dominate. For the other component, roads, which includes the remaining nodes, low centrality nodes dominate. We find also that the distribution of the centrality for the infinite incipient percolation cluster satisfies a power law, with an exponent smaller than that for the entire MST. The significance of this finding is that one can improve significantly the global transport by improving a tiny fraction of the network, the superhighways.

Abstract: We study the betweenness centrality of fractal and nonfractal scale-free network models as well as real networks. We show that the correlation between degree and betweenness centrality C of nodes is much weaker in fractal network models compared to nonfractal models. We also show that nodes of both fractal and nonfractal scale-free networks have power-law betweenness centrality distribution P(C) approximately C(-delta). We find that for nonfractal scale-free networks delta=2, and for fractal scale-free networks delta=2-1/dB, where dB is the dimension of the fractal network. We support these results by explicit calculations on four real networks: pharmaceutical firms (N=6776), yeast (N=1458), WWW (N=2526), and a sample of Internet network at the autonomous system level (N=20566), where N is the number of nodes in the largest connected component of a network. We also study the crossover phenomenon from fractal to nonfractal networks upon adding random edges to a fractal network. We show that the crossover length l*, separating fractal and nonfractal regimes, scales with dimension dB of the network as p(-1/dB), where p is the density of random edges added to the network. We find that the correlation between degree and betweenness centrality increases with p.

Abstract: This paper reports results from a laboratory experiment on network formation among heterogeneous agents. The experimental design extends the Bala-Goyal (2000) model of network formation with decay and two-way flow of benefits by allowing for agents with lower linking costs or higher benefits to others. Furthermore, agents' types may be common knowledge or private information. In all treatments, the (efficient) equilibrium network has a "star" structure. With homogeneous agents, equilibrium predictions fail completely. In contrast, with heterogeneous agents stars frequently occur, often with the high-value or low-cost agent in the center. Stars are not born but rather develop: with a high-value agent, the network's centrality, stability, and efficiency all increase over time. Probit estimations based on best-response behaviour and other-regarding preferences are used to analyze individual linking behavior. Our results suggest that heterogeneity is a major determinant for the predominance of star-like structures in real-life social networks.

Abstract: Keywords characterize the topics discussed in a document. Extracting a small set of keywords from a single document is an important problem in text mining. We propose a hybrid structural and statistical approach to extract keywords. We represent the given document as an undirected graph, whose vertices are words in the document and the edges are labeled with a dissimilarity measure between two words, derived from the frequency of their co-occurrence in the document. We propose that central vertices in this graph are candidates as keywords. We model importance of a word in terms of its centrality in this graph. Using graph-theoretical notions of vertex centrality, we suggest several algorithms to extract keywords from the given document. We demonstrate the effectiveness of the proposed algorithms on real-life documents.

Abstract: A set covering greedy algorithm is proposed for solving the influence maximization problem in social networks. Two information diffusion models are considered: Independent Cascade Model and Linear Threshold Model. The proposed algorithm is compared with traditional maximization algorithms such as simple greedy and degree centrality using three data sets. In addition, an algorithm for mapping social networks is proposed, which allows visualizing the infection process and how the different algorithms evolve. The proposed approach is useful for mining large social networks.

Abstract: In this paper, we propose a method for rapid computation of group betweenness centrality whose running time (after preprocessing) does not depend on network size. The calculation of group betweenness centrality is computationally demanding and, therefore, it is not suitable for applications that compute the centrality of many groups in order to identify new properties. Our method is based on the concept of path betweenness centrality defined in this paper. We demonstrate how the method can be used to find the most prominent group. Then, we apply the method for epidemic control in communication networks. We also show how the method can be used to evaluate distributions of group betweenness centrality and its correlation with group degree. The method may assist in finding further properties of complex networks and may open a wide range of research opportunities.

Abstract: Network science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution (1-4) in many disparate fields from large communication networks (5,6), transportation infrastructures (7) and social communities (8,9) to biological systems (1,10,11). Even though new highthroughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies (12-14) create a promising framework to combat complex multigenetic disorders like cancer or diabetes. Here, we investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known drug-therapy relationships. Our results show that the key paths in this network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-network composed by drugs with high centrality measures (15), which represent the structural back-bone of the drug-therapy system and act as hubs routing information between distant parts of the network. These findings provide for the first time a global map of the largescale organization of all known drugs and associated therapies, bringing new insights on possible strategies for future drug development. Special attention should be given to drugs which combine the two properties of (a) having a high centrality value and (b) acting on multiple targets.

Abstract: This paper assesses impact of accessibility corresponding to three distinct modes of urban transportation. The Alonso hypothesis of residents being fully compensated for rents increasing with proximity to CBD by employment opportunities is tested by application of a hedonic model using micro level data to explain standard land values in Berlin. Access to employment as well as location endowments with natural amenities and publicly and privately provided services are captured by potentiality variables. Similarly, impact of population potentiality is assessed for business properties. Accessibility generated by urban rail network is clearly found to have positive impacts on property prices and fully explains attractiveness of urban centrality for business. For residential properties, however, impact of proximity to CBD cannot be completely explained by employment opportunities revealing that the CBD provides additional services valued by residents.

Abstract: This article explores some of the conditions and consequences of the centrality of the figure of the consumer in recent public service reform in the UK. New Labour's view of the modern world as being defined in part by the rise of a consumer culture or consumer society locates the figure of the consumer at the heart of its programme of public service reform in the decade from 1997. Drawing on a recent study of public services, the article considers the impact of this consumerist model of reform on the relationships between public service organizations and their publics, drawing out three particular sites of strain that mark the shifting relationships between the public and services. These are the tensions between rights, resources and rationing; the links and disjunctures between choice and voice; and the tangled formations of knowledge and power.

Abstract: Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.

Abstract: We consider a model of content contribution in peer-to-peer networks with linear quadratic payoffs and very general interaction patterns. We find that Nash equilibria of this game always exist; moreover, they are computable by solving a linear complementarity problem. The equilibrium is unique when goods are strategic complements or weak substitutes and contributions are proportional to a network centrality measure called the Bonacich index. In the case of public goods, the equilibrium is non-unique and characterized by k-order maximal independent sets. The structure of optimal networks is always star-like when the game exhibits strict or weak complements. Under public good scenarios, while star-like networks remain optimal in the best case, they also yield the worst-performing equilibria. We also discuss a network-based policy for improving the equilibrium performance of networks by the exclusion of a single player.

Abstract: This article proposes a neo-structural approach to the multi-level dimension of organizational and social life. Our study explores multi-level networks that observe two systems of superposed and partially interlocked interdependencies, the first being inter-organizational, the second interindividual. We propose a method of structural linked design as an articulation for these levels. The method is composed of two major steps : first, we separately examine the complete networks at the two different levels. Second, we articulate the two networks in relation to one another using systematic information about the membership of each individual in the first network (interindividual) to one of the organizations in the second network (inter-organizational). This dualpositioning, or the linked design approach, is carried out in an empirical study examining performance variations within the « elite » of French cancer research in 1999. By looking at measures of centrality, we identify the actors that these top researchers consider as central or peripheral at the inter-individual level (the big and the little fish among the elite), and the laboratories that the research directors consider as central or peripheral at the inter-organizational level (the big and the little ponds among all the laboratories conducting cancer research in France at that time). In addition to the rather trivial report of the competitive advantage of big fish in big ponds (particularly because of the advantage of size for laboratories in this domain of research), we use measurements of scientific performance to identify « catching up » strategies that the smallest fish use in this system. We suggest that this method offers new insights into the multilevel dimension of complex systems of interdependencies, and also into the way in which actors manage these interdependencies. We believe that this understanding adds a new dimension to the sociological exploration of the determinants of performance, of meso-level phenomena such as institutional change, or macro-level phenomena such as social inequalities.

Abstract: In this article we establish new results on the components of the principal eigenvector in an undirected graph. Those results are particularly significant in relation to the concept of centrality in social networks. In particular degree centrality and eigenvector centrality are compared. We find further conditions, based on the spectral radius, on which nodes with highest degree centrality are also the most eigencentral.

Abstract: In this paper we seek to improve understanding of the structure of human mobility, and to use this in the design of forwarding algorithms for Delay Tolerant Networks for the dissemination of data amongst mobile users. Cooperation binds but also divides human society into communities. Members of the same community interact with each other preferentially. There is structure in human society. Within society and its communities, individuals have varying popularity. Some people are more popular and interact with more people than others; we may call them hubs. Popularity ranking is one facet of the population. In many physical networks, some nodes are more highly connected to each other than to the rest of the network. The set of such nodes are usually called clusters, communities, cohesive groups or modules. There is structure to social networking. Different metrics can be used such as information flow, Freeman betweenness, closeness and inference power, but for all of them, each node in the network can be assigned a global centrality value. What can be inferred about individual popularity, and the structure of human society from measurements within a network? How can the local and global characteristics of the network be used practically for information dissemination? We present and evaluate a sequence of designs for forwarding algorithms for Pocket Switched Networks, culminating in Bubble, which exploit increasing levels of information about mobility and interaction.

Abstract: We present an algebraic account of the Tongan kinship terminology (TKT) that provides an insightful journey into the fabric of Tongan culture. We begin with the ethnographic account of a social event. e account provides us with the activities of that day and the centrality of kin relations in the event, but it does not inform us of the conceptual system that the participants bring with them. Rather, it is a slice in time of an ongoing dynamic process that links behavior with a conceptual system of kin relations and vice versa. To understand this interplay, we need an account of the underlying conceptual system that is being activated during the event. Thus, we introduce a formal, algebraically based account of TKT. is account brings to the fore the underlying logic of TKT and allows us to distinguish between features of the kinship system that arise from the logic of TKT as a generative structure and features that must have arisen through cultural intervention.

Abstract: The pre- and postdownsizing information flow and postdownsizing turnover intentions of downsizing survivors were examined in the corporate office of an international hotel company. Using a combination of network analysis and path analysis, the relationship between changes in downsizing survivors' betweenness centrality and perceptions of information adequacy relative to reported turnover intentions were examined across two postdownsizing time periods. Results of the path analyses provided general support for the model as hypothesized, indicating in postdownsizing periods that changes to network members' network centrality positively influenced changes in their perceptions of information adequacy, which then negatively influenced their turnover intentions. The article concludes with a discussion of the support for the hypotheses and the study's limitations and pragmatic implications.

Abstract: We consider methodologies for managing risk in a telecommunication net- work based on identification of the critical nodes. The objec tive is to identify a set of vertices with a specified cardinality whose deletion result s is maximum number of discon- nected components. This is referred to as the CRITICAL NODE PROBLEM, and finds ap- plication in epidemic control, telecommunications, and military tactical planning, among others. From a telecommunication perspective, the set of critical nodes helps determine which players should be removed from the network in the event of a virus outbreak. Con- versely, in order to maintain maximum global connectivity, it should be ensured that the critical nodes remain intact. In this chapter, we review the recent work in this area and examine formulations based on integer linear programming. In this chapter, we study two variants of the CRITICAL NODE PROBLEM. In general, the objective of the CRITICAL NODE PROBLEM (CNP) is to find a set of k nodes in a graph whose deletion results in the maximum network fragmentation. By this we mean, maximize the number of components in thek-vertex deleted subgraph. Studies carried out in this line include those by Bavelas (6) and Freeman (15) which emphasize node centrality and prestige, both of which are usually functions of a nodes degree. However, they lacked applications to problems which emphasized network fragmentation and connectivity. We can apply the CNP to the problem of jamming wired telecommunication networks by identifying the critical nodes and suppressing the communication on these nodes. This will result in the maximum number of disconnected components which are unable to communi- cate with each other. The CNP can also be applied to the study of covert terrorist networks , where a certain number of individuals have to be identified wh ose deletion would result in the maximum breakdown of communication between individuals in the network (24). Likewise in order to stop the spreading of a virus over a telecommunication network, one can identify the critical nodes of the graph and take them offl ine. The CNP also finds applications in network immunization (9, 34) wher e mass vaccina- tion is an expensive process and only a specific number of peop le, modeled as nodes of a graph, can be vaccinated. The immunized nodes cannot propagate the virus and the goal is to identify the individuals to be vaccinated in order to redu ce the overall transmissibility of the virus. There are several vaccination strategies in the l iterature (see e.g. (9, 34)) offering control of epidemic outbreaks; however, none of the proposed are optimal strategies. The vaccination strategies suggested emphasize the centrality of nodes as a major factor rather than critical nodes whose deletion will maximize disconnectivity of the graph. Deletion of central nodes may not guarantee a fragmentation of the network or even disconnectivity, in which case disease transmission cannot be prevented. Of course, owing to its dynamic stature, the relationships between people, represented by edges in the social network are transient and there is a constant rewiring between nodes, and alternate relationships could