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.

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.

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 near-linear 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]...

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.

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.

Abstract not found

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

Centrality indices are an important tool in network analysis, and many of them are derived from the set of all shortest paths of the underlying graph. The so-called betweenness centrality index is essential for the analysis of social networks, but most costly to compute. Currently, the fastest known algorithms require Theta(n³) time and Theta(n²) space, where n is the number of vertices. Motivated by the fast-growing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run in O(n(m + n)) or O(n(m + n log n)) time on unweighted or weighted graphs, respectively, where m is the number of edges. Since these algorithms simply augment single-source shortest-paths computations, all standard centrality indices based on shortest paths can now be computed uniformly in one framework. Experimental evidence is provided that this substantially increases the range of network...

) Ulrik Brandes 1 , Patrick Kenis 2 , and Dorothea Wagner 1 1 University of Konstanz, Faculty of Mathematics and Computer Science 99 9999 9999 9999 9999 9999 9999 9999 9999 9999 -- %stopped_push --nostring 2 Free University, Faculty of Social and Cultural Sciences, Amsterdam * + $,'-./%. Abstract. We report on first results of a cooperation aiming at the usage of graph drawing techniques to convey domain-specific information contained in policy or, more general, social networks. Policy network analysis is an approach to study policy making processes, structures and outcomes, thereby concentrating on the analysis of relations between policy actors. An important operational concept for the analysis of policy networks is centrality, i.e. the distinction of actors according to their importance in a relational structure. Matching structural with geometric centrality we incorporate the aggregated values of centrality measures into a layout...

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 effective means for search engines to cope with the explosive growth and diversification of the Web.