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97
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 305 (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.
Applying Social Network Analysis to the Information in CVS Repositories
"... The huge quantities of data available in the CVS repositories of large, longlived libre (free, open source) software projects, and the many interrelationships among those data offer opportunities for extracting large amounts of valuable information about their structure, evolution and internal proc ..."
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Cited by 41 (0 self)
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The huge quantities of data available in the CVS repositories of large, longlived libre (free, open source) software projects, and the many interrelationships among those data offer opportunities for extracting large amounts of valuable information about their structure, evolution and internal processes. Unfortunately, the sheer volume of that information renders it almost unusable without applying methodologies which highlight the relevant information for a given aspect of the project. In this paper, we propose the use of a well known set of methodologies (social network analysis) for characterizing libre software projects, their evolution over time and their internal structure. In addition, we show how we have applied such methodologies to real cases, and extract some preliminary conclusions from that experience.
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 34 (10 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.
Communicating centrality in policy network drawings
 IEEE Transactions on Visualization and Computer Graphics
"... ..."
Predicting Defects using Network Analysis on Dependency Graphs
"... In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have ..."
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Cited by 32 (3 self)
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In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10 % points higher than for models built from complexity metrics. In addition, network measures could identify 60 % of the binaries that the Windows developers considered as critical—twice as many as identified by complexity metrics. Categories and Subject Descriptors D.2.8 [Software Engineering]: Metrics—Performance measures, Process metrics, Product metrics. D.2.9 [Software Engineering]: Management—Software quality assurance (SQA)
The connectivity structure; Giant strong component and centrality of metabolic networks
 Bioinformatics 2003
"... Motivation: Structural and functional analysis of genomebased largescale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rationa ..."
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Cited by 32 (0 self)
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Motivation: Structural and functional analysis of genomebased largescale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important. Results: In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected subnetwork, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced
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.
Social Network Analysis with sna
"... Modern social network analysis—the analysis of relational data arising from social systems—is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing envir ..."
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Cited by 13 (0 self)
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Modern social network analysis—the analysis of relational data arising from social systems—is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown.
Using graphbased metrics with empirical risk minimization to speed up active learning on networked data
 Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2009
"... Active and semisupervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semisupervised learning algorithm that uses Gaussian fields and harmonic functions. This classifier is relational in nature: it relies on h ..."
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Cited by 11 (1 self)
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Active and semisupervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semisupervised learning algorithm that uses Gaussian fields and harmonic functions. This classifier is relational in nature: it relies on having the data presented as a partially labeled graph (also known as a withinnetwork learning problem). This work showed yet again that empirical risk minimization (ERM) was the best method to find the next instance to label and provided an efficient way to compute ERM with the semisupervised classifier. The computational problem with ERM is that it relies on computing the risk for all possible instances. If we could limit the candidates that should be investigated, then we can speed up active learning considerably. In the case where the data is graphical in nature, we can leverage the graph structure to rapidly identify instances that are likely to be good candidates for labeling. This paper describes a novel hybrid approach of using of community finding and social network analytic centrality measures to identify good candidates for labeling and then using ERM to find the best instance in this candidate set. We show on realworld data that we can limit the ERM computations to a fraction of instances with comparable performance.