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26
Approximating Betweenness Centrality
, 2007
"... Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationallyexpensive to exactly determine betweenness; currently the fastestknown algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n 2 log n) time for weighted ..."
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Cited by 25 (5 self)
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Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationallyexpensive to exactly determine betweenness; currently the fastestknown algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n 2 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 singlesource shortest path computations for vertices with high centrality. We conduct an extensive experimental study on realworld graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biological networks, road networks and web crawls.
Better Approximation of Betweenness Centrality
"... Estimating the importance or centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications, ..."
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Cited by 18 (0 self)
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Estimating the importance or centrality of the nodes in large networks has recently attracted increased interest. Betweenness is one of the most important centrality indices, which basically counts the number of shortest paths going through a node. Betweenness has been used in diverse applications, e.g., social network analysis or route planning. Since exact computation is prohibitive for large networks, approximation algorithms are important. In this paper, we propose a framework for unbiased approximation of betweenness that generalizes a previous approach by Brandes. Our best new schemes yield significantly better approximation than before for many real world inputs. In particular, we also get good approximations for the betweenness of unimportant nodes.
Visualization of social and other scalefree networks
 IN PROC. OF IEEE INFOVIS
, 2008
"... This paper proposes novel methods for visualizing specifically the large powerlaw graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more ..."
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Cited by 17 (1 self)
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This paper proposes novel methods for visualizing specifically the large powerlaw graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network’s underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scalefree graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.
Pragmatic Evaluation of Folksonomies
"... Recently, a number of algorithms have been proposed to obtain hierarchical structures — socalled folksonomies — from social tagging data. Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as: (a) Navigating social tagging systems and (b) Acquiring se ..."
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Cited by 9 (6 self)
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Recently, a number of algorithms have been proposed to obtain hierarchical structures — socalled folksonomies — from social tagging data. Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as: (a) Navigating social tagging systems and (b) Acquiring semantic relationships between tags. While the promises and pitfalls of the latter have been studied to some extent, we know very little about the extent to which folksonomies are pragmatically useful for navigating social tagging systems. This paper sets out to address this gap by presenting and applying a pragmatic framework for evaluating folksonomies. We model exploratory navigation of a tagging system as decentralized search on a network of tags. Evaluation is based on the fact that the performance of a decentralized search algorithm depends on the quality of the background knowledge used. The key idea of
Ranking of Closeness Centrality for LargeScale Social Networks
"... Abstract. Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate ..."
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Cited by 8 (0 self)
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Abstract. Closeness centrality is an important concept in social network analysis. In a graph representing a social network, closeness centrality measures how close a vertex is to all other vertices in the graph. In this paper, we combine existing methods on calculating exact values and approximate values of closeness centrality and present new algorithms to rank the topk vertices with the highest closeness centrality. We show that under certain conditions, our algorithm is more efficient than the algorithm that calculates the closenesscentralities of all vertices. 1
Analysis of a Real Online Social Network Using Semantic Web Frameworks
"... Abstract. Social Network Analysis (SNA) provides graph algorithms to characterize the structure of social networks, strategic positions in these networks, specific subnetworks and decompositions of people and activities. Online social platforms like Facebook form huge social networks, enabling peop ..."
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Cited by 7 (3 self)
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Abstract. Social Network Analysis (SNA) provides graph algorithms to characterize the structure of social networks, strategic positions in these networks, specific subnetworks and decompositions of people and activities. Online social platforms like Facebook form huge social networks, enabling people to connect, interact and share their online activities across several social applications. We extended SNA operators using semantic web frameworks to include the semantics of these graphbased representations when analyzing such social networks and to deal with the diversity of their relations and interactions. We present here the results of this approach when it was used to analyze a real social network with 60,000 users connecting, interacting and sharing content.
One tag to bind them all : Measuring term abstractness in social metadata
 Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), Heraklion
, 2011
"... Abstract. Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering lightweight ontologies. For example, existing measures of tag s ..."
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Cited by 6 (5 self)
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Abstract. Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering lightweight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several largescale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and WikiTaxonomy. Then, we introduce and apply several folksonomybased methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of lightweight ontologies from tagging data.
Centralities: Capturing the Fuzzy Notion of Importance in Social Graphs
 SNS
, 2009
"... The increase of interest in the analysis of contemporary social networks, for both academic and economic reasons, has highlighted the inherent difficulties in handling large and complex structures. Among the tools provided by researchers for network analysis, the centrality notion, capturing the imp ..."
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Cited by 4 (1 self)
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The increase of interest in the analysis of contemporary social networks, for both academic and economic reasons, has highlighted the inherent difficulties in handling large and complex structures. Among the tools provided by researchers for network analysis, the centrality notion, capturing the importance of individuals in a graph, is of particular interest. Despite many definitions and implementations of centrality, no clear advantage is given to a particular paradigm for the study of social network characteristics. In this paper we review, compare and highlight the strengths of different definitions of centralities in contemporary social networks. 1.
QUBE: a Quick algorithm for Updating BEtweenness centrality
, 2012
"... 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 ..."
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Cited by 3 (0 self)
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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
Towards Mining Semantic Maturity in Social Bookmarking Systems
"... Abstract. The existence of emergent semantics within social metadata (such as tags in bookmarking systems) has been proven by a large number of successful approaches making the implicit semantic structures explicit. However, much less attention has been given to the factors which influence the “matu ..."
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Cited by 1 (1 self)
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Abstract. The existence of emergent semantics within social metadata (such as tags in bookmarking systems) has been proven by a large number of successful approaches making the implicit semantic structures explicit. However, much less attention has been given to the factors which influence the “maturing ” process of these structures over time. A natural hypothesis is that tags become semantically more and more mature whenever many users use them in the same contexts. This would allow to describe a tag by a specific and informative “semantic fingerprint ” in the context of tagged resoures. However, the question of assessing the quality of such fingerprints has been seldomly addressed. In this paper, we provide a systematic approach of mining semantic maturity profiles within folksonomybased tag properties. Our ultimate goal is to provide a characterization of “mature tags”. Additionally, we consider semantic information about the tags as a goldstandard source for the characterization of the collected results. Our initial results suggest that a suitable composition of tag properties allows the identification of more mature tag subsets. The presented work has implications for a number of problems related to social tagging systems, including tag ranking, tag recommendation, and the capturing of lightweight ontologies from tagging data. 1