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47
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 546 (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.
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
 IEEE TPAMI
"... Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a longstanding research objective for natural language processing. In this paper we are concerned with graphbased algorithms for largescale WSD. Under this framework, finding the ..."
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Cited by 62 (15 self)
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Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a longstanding research objective for natural language processing. In this paper we are concerned with graphbased algorithms for largescale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most “important ” node among the set of graph nodes representing its senses. We introduce a graphbased WSD algorithm which has few parameters and does not require sense annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets, and show that our graphbased approach performs comparably to the state of the art.
Centrality estimation in large networks
 INTL. JOURNAL OF BIFURCATION AND CHAOS, SPECIAL ISSUE ON COMPLEX NETWORKS’ STRUCTURE AND DYNAMICS
, 2007
"... Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (SSSP) problem from every node. Therefore, exact computation is ..."
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Cited by 54 (0 self)
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Centrality indices are an essential concept in network analysis. For those based on shortestpath distances the computation is at least quadratic in the number of nodes, since it usually involves solving the singlesource shortestpaths (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.
Graph connectivity measures for unsupervised word sense disambiguation
 ICJAI
, 2007
"... Word sense disambiguation (WSD) has been a longstanding research objective for natural language processing. In this paper we are concerned with developing graphbased unsupervised algorithms for alleviating the data requirements for large scale WSD. Under this framework, finding the right sense for ..."
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Cited by 34 (3 self)
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Word sense disambiguation (WSD) has been a longstanding research objective for natural language processing. In this paper we are concerned with developing graphbased unsupervised algorithms for alleviating the data requirements for large scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most “important ” node among the set of graph nodes representing its senses. We propose a variety of measures that analyze the connectivity of graph structures, thereby identifying the most relevant word senses. We assess their performance on standard datasets, and show that the best measures perform comparably to stateoftheart. 1
INEQUALITIES
, 2009
"... Publication details, including instructions for authors and subscription information: ..."
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Cited by 23 (2 self)
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Publication details, including instructions for authors and subscription information:
Predicting influential users in online social networks
 IN: SNAKDD: PROCEEDINGS OF KDD WORKSHOP ON SOCIAL NETWORK ANALYSIS
, 2010
"... ..."
Social network analysis: A methodological introduction
 Asian Journal of Social Psychology
"... Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single n ..."
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Cited by 18 (1 self)
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Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individuallevel covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions. Key words: relational data, social network analysis, social structure.
Betweenness Centrality: Algorithms and Lower Bounds
, 2008
"... One of the most fundamental problems in largescale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely used metric to measure the importance of a node in a network. In this paper, we present a randomized parallel algorithm and ..."
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Cited by 16 (0 self)
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One of the most fundamental problems in largescale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely used metric to measure the importance of a node in a network. In this paper, we present a randomized parallel algorithm and an algebraic method for computing betweenness centrality of all nodes in a network. We prove that any pathcomparison based algorithm cannot compute betweenness in less than O(nm) time.