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32
A Faster Algorithm for Betweenness Centrality
 Journal of Mathematical Sociology
, 2001
"... The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network. ..."
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Cited by 291 (5 self)
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The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require #(n ) time and #(n ) space, where n is the number of actors in the network.
Centrality and Network Flow
"... Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest p ..."
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Cited by 58 (1 self)
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Centrality measures, or at least our interpretations of these measures, make implicit assumptions about the manner in which things flow through a network. For example, some measures count only geodesic paths, apparently assuming that whatever flows through the network only moves along the shortest possible paths. This paper lays out a typology of network flows based on two dimensions of variation, namely, the kinds of trajectories that traffic may follow (geodesics, paths, trails or walks), and the method of spread (broadcast, serial replication, or transfer). Measures of centrality are then matched to the kinds of flows they are appropriate for. Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic. It is shown that the offtheshelf formulas for centrality measures are fully applicable only for the specific flow processes they are designed for, and that when they are applied to other flow processes they get the “wrong” answer. It is noted that the most commonly used centrality measures are not appropriate for most of the flows we are routinely interested in. A key claim made in this paper is that centrality measures can be regarded as generating expected values for certain kinds of node outcomes (such as speed and frequency of reception) given implicit models of how things flow.
Communicating Centrality in Policy Network Drawings
, 2003
"... We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational co ..."
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Cited by 33 (10 self)
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We introduce a network visualization technique that supports an analytical method applied in the social sciences. Policy network analysis is an approach to study policy making structures, processes, and outcomes, thereby concentrating on relations between policy actors. An important operational concept for the analysis of policy networks is the notion of centrality, i.e., the distinction of actors according to their importance in a relational structure. We integrate this measure in a layout model for networks by mapping structural to geometric centrality. Thus, centrality values and network data can be presented simultaneously and explored interactively.
Fast Approximation of Centrality
 Journal of Graph Algorithms and Applications
, 2001
"... Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For g ..."
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Cited by 32 (0 self)
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Social studies researchers use graphs to model group activities in social networks. An important property in this context is the centrality of a vertex: the inverse of the average distance to each other vertex. We describe a randomized approximation algorithm for centrality in weighted graphs. For graphs exhibiting the small world phenomenon, our method estimates the centrality of all vertices with high probability within a (1 + #) factor in nearlinear time. 1 Introduction In social network analysis, the vertices of a graph represent agents in a group and the edges represent relationships, such as communication or friendship. The idea of applying graph theory to analyze the connection between the structural centrality and group process was introduced by Bavelas [4]. Various measurement of centrality [7, 14, 15] have been proposed for analyzing communication activity, control, or independence within a social network. We are particularly interested in closeness centrality [5, 6, 24]...
Visual Ranking of Link Structures
 Journal of Graph Algorithms and Applications
, 2003
"... Methods for ranking World Wide Web resources according to their position in the link structure of the Web are receiving considerable attention, because they provide the first e#ective means for search engines to cope with the explosive growth and diversification of the Web. Closely related metho ..."
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Cited by 20 (3 self)
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Methods for ranking World Wide Web resources according to their position in the link structure of the Web are receiving considerable attention, because they provide the first e#ective means for search engines to cope with the explosive growth and diversification of the Web. Closely related methods have been used in other disciplines for quite some time.
Epistemic communities: Description and hierarchic categorization
 Mathematical Population Studies
, 2005
"... Social scientists have shown an increasing interest in understanding the structure of knowledge communities, and particularly the organization of “epistemic communities”, that is groups of agents sharing common knowledge concerns. However, most existing approaches are based only on either social rel ..."
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Cited by 7 (5 self)
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Social scientists have shown an increasing interest in understanding the structure of knowledge communities, and particularly the organization of “epistemic communities”, that is groups of agents sharing common knowledge concerns. However, most existing approaches are based only on either social relationships or semantic similarity, while there has been roughly no attempt to link social and semantic aspects. In this paper, we introduce a formal framework addressing this issue and propose a method based on Galois lattices (or concept lattices) for categorizing epistemic communities in an automated and hierarchically structured fashion. Suggesting that our process allows us to rebuild a whole community structure and taxonomy, and notably fields and subfields gathering a certain proportion of agents, we eventually apply it to empirical data to exhibit these alleged structural properties, and successfully compare our results with categories spontaneously given by domain experts.
Effects of Sociogram Drawing Conventions and Edge Crossings in Social Network Visualization
 Journal of Graph Algorithms and Applications
, 2007
"... This paper describes a withinsubjects experiment. In this experiment, the effects of different spatial layouts on human sociogram perception are examined. We compare the relative effectiveness of five sociogram drawing conventions in communicating underlying network substance, based on user task pe ..."
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Cited by 6 (0 self)
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This paper describes a withinsubjects experiment. In this experiment, the effects of different spatial layouts on human sociogram perception are examined. We compare the relative effectiveness of five sociogram drawing conventions in communicating underlying network substance, based on user task performance and personal preference. We also explore the impact of edge crossings, a widely accepted readability aesthetic. Both objective performance and subjective questionnaire measures are employed in the study. Subjective data are gathered based on the methodology of Purchase et al. [70], while objective data are collected through an online system. We found that 1) both edge crossings and drawing conventions pose significant effects on user preference and task performance of finding groups, but neither has much impact on the perception of actor status. On the other hand, node positioning and angular resolution may be more important in perceiving actor status. In visualizing social networks, it is important to note that the techniques that are highly preferred by users do not necessarily lead to best task performance. 2) subjects have a strong preference of placing nodes on the top or in the center to highlight importance, and clustering nodes in the same group and separating clusters to highlight groups. They have tendency to believe that nodes on the top or in the center are more important, and nodes in close proximity belong to the same group. Some preliminary recommendations for sociogram design and hypotheses about human reading behavior are proposed.
Preference fusion when the number of alternatives exceeds two: indirect scoring procedures
, 2006
"... ..."
Centrality and AIDS
 Connections
, 1995
"... Techniques is a regular column devoted to techniques of data construction, management, interpretation and analysis. Contributions are appreciated. Centrality measures are commonly described as indices of prestige, prominence, importance, and power — the four Ps. However, this sort of interpretation ..."
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Cited by 4 (1 self)
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Techniques is a regular column devoted to techniques of data construction, management, interpretation and analysis. Contributions are appreciated. Centrality measures are commonly described as indices of prestige, prominence, importance, and power — the four Ps. However, this sort of interpretation seems inappropriate in the case of sexual networks. In this column, I consider the interpretation of centrality measures in sexual networks, and more generally in the context of any kind of network diffusion. For simplicity, I will assume that the data 1 consist of a discrete symmetric social relation
Identification of influential spreaders in complex networks
, 2010
"... Networks portray a multitude of interactions through which people meet, ideas are spread and infectious diseases propagate within a society 1–5. Identifying the most efficient ‘spreaders’ in a network is an important step towards optimizing the use of available resources and ensuring the more effici ..."
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Cited by 3 (0 self)
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Networks portray a multitude of interactions through which people meet, ideas are spread and infectious diseases propagate within a society 1–5. Identifying the most efficient ‘spreaders’ in a network is an important step towards optimizing the use of available resources and ensuring the more efficient spread of information. Here we show that, in contrast to common belief, there are plausible circumstances where the best spreaders do not correspond to the most highly connected or the most central people 6–10. Instead, we find that the most efficient spreaders are those located within the core of the network as identified by the kshell decomposition analysis 11–13, and that when multiple spreaders are considered simultaneously the distance between them becomes the crucial parameter that determines the extent of the spreading. Furthermore, we show that infections persist in the highk shells of the network in