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255
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 32 (11 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 28 (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.
SHARC: Fast and robust unidirectional routing
 IN: WORKSHOP ON ALGORITHM ENGINEERING AND EXPERIMENTS (ALENEX
, 2008
"... During the last years, impressive speedup techniques for Dijkstra’s algorithm have been developed. Unfortunately, the most advanced techniques use bidirectional search which makes it hard to use them in scenarios where a backward search is prohibited. Even worse, such scenarios are widely spread, e ..."
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Cited by 28 (15 self)
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During the last years, impressive speedup techniques for Dijkstra’s algorithm have been developed. Unfortunately, the most advanced techniques use bidirectional search which makes it hard to use them in scenarios where a backward search is prohibited. Even worse, such scenarios are widely spread, e.g., timetableinformation systems or timedependent networks. In this work, we present a unidirectional speedup technique which competes with bidirectional approaches. Moreover, we show how to exploit the advantage of unidirectional routing for fast exact queries in timetable information systems and for fast approximative queries in timedependent scenarios. By running experiments on several inputs other than road networks, we show that our approach is very robust to the input.
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.
A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets
, 2009
"... We present a new lockfree parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging fr ..."
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Cited by 24 (7 self)
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We present a new lockfree parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging highperformance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Threadstorm processor. For a smallworld network of 268 million vertices and 2.147 billion edges, the 16processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2 × performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive realworld datasets by computing approximate betweenness centrality for the large IMDb movieactor network. 1.
Information Dynamics in the Networked World
 In Lecture Notes in Physics
, 2004
"... Summary. We review three studies of information flow in social networks that help reveal their underlying social structure, how information spreads through them and why small world experiments work. 1 ..."
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Cited by 24 (3 self)
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Summary. We review three studies of information flow in social networks that help reveal their underlying social structure, how information spreads through them and why small world experiments work. 1
A statistical approach to the traceroutelike exploration of networks: theory and simulations
, 2004
"... ..."
Mixing patterns and community structure in networks
 in Statistical Mechanics of Complex Networks
"... Common experience suggests that many networks might possess community structure—division of vertices into groups, with a higher density of edges within groups than between them. Here we describe a new computer algorithm that detects structure of this kind. We apply the algorithm to a number of realw ..."
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Cited by 21 (1 self)
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Common experience suggests that many networks might possess community structure—division of vertices into groups, with a higher density of edges within groups than between them. Here we describe a new computer algorithm that detects structure of this kind. We apply the algorithm to a number of realworld networks and show that they do indeed possess nontrivial community structure. We suggest a possible explanation for this structure in the mechanism of assortative mixing, which is the preferential association of network vertices with others that are like them in some way. We show by simulation that this mechanism can indeed account for community structure. We also look in detail at one particular example of assortative mixing, namely mixing by vertex degree, in which vertices with similar degree prefer to be connected to one another. We propose a measure for mixing of this type which we apply to a variety of networks, and also discuss the implications for network structure and the formation of a giant component in assortatively mixed networks. 1
The Combinatorial BLAS: Design, Implementation, and Applications
, 2010
"... This paper presents a scalable highperformance software library to be used for graph analysis and data mining. Large combinatorial graphs appear in many applications of highperformance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse mat ..."
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Cited by 21 (9 self)
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This paper presents a scalable highperformance software library to be used for graph analysis and data mining. Large combinatorial graphs appear in many applications of highperformance computing, including computational biology, informatics, analytics, web search, dynamical systems, and sparse matrix methods. Graph computations are difficult to parallelize using traditional approaches due to their irregular nature and low operational intensity. Many graph computations, however, contain sufficient coarse grained parallelism for thousands of processors, which can be uncovered by using the right primitives. We describe the Parallel Combinatorial BLAS, which consists of a small but powerful set of linear algebra primitives specifically targeting graph and data mining applications. We provide an extendible library interface and some guiding principles for future development. The library is evaluated using two important graph algorithms, in terms of both performance and easeofuse. The scalability and raw performance of the example applications, using the combinatorial BLAS, are unprecedented on distributed memory clusters.