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A distributed algorithm for minimumweight spanning trees
, 1983
"... A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm and exchange ..."
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Cited by 443 (3 self)
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A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm
Finding the k Most Vital Edges with Respect to Minimum Spanning Tree
 Acta Informatica
, 1995
"... For a connected, undirected and weighted graph G = (V; E), the problem of finding the k most vital edges of G with respect to minimumspanning tree is to find k edges in G whose removal will cause greatest weight increase in the minimum spanning tree of the remaining graph. This problem is known to ..."
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Cited by 17 (0 self)
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For a connected, undirected and weighted graph G = (V; E), the problem of finding the k most vital edges of G with respect to minimumspanning tree is to find k edges in G whose removal will cause greatest weight increase in the minimum spanning tree of the remaining graph. This problem is known
Community detection in graphs
, 2009
"... The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of th ..."
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Cited by 801 (1 self)
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The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices
Optimal Algorithms to Find the Most Vital Edge of a Minimum Spanning Tree
, 1995
"... The problem of finding the most vital edge with respect to a minimum spanning tree of a given connected and weighted graph (with m edges and n vertices) is considered. New sequential and parallel algorithms (3 each) for the problem are proposed, and a lower bound\Omega\Gamma m) is established. We c ..."
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Cited by 3 (0 self)
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The problem of finding the most vital edge with respect to a minimum spanning tree of a given connected and weighted graph (with m edges and n vertices) is considered. New sequential and parallel algorithms (3 each) for the problem are proposed, and a lower bound\Omega\Gamma m) is established. We
Improved parallel algorithms for finding the most vital edge of a graph with respect to minimum spanning tree
, 1995
"... Let G be a connected, undirected and weighted graph with n vertices and m edges. A most vital edge of G with respect to minimum spanning tree is an edge whose removal from G will cause the greatest weightincrease in the minimum spanning tree of the remaining graph. This paper presents fast parallel ..."
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Cited by 1 (1 self)
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Let G be a connected, undirected and weighted graph with n vertices and m edges. A most vital edge of G with respect to minimum spanning tree is an edge whose removal from G will cause the greatest weightincrease in the minimum spanning tree of the remaining graph. This paper presents fast
Finding community structure in networks using the eigenvectors of matrices
, 2006
"... We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible div ..."
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Cited by 500 (0 self)
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We consider the problem of detecting communities or modules in networks, groups of vertices with a higherthanaverage density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as “modularity ” over possible
A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
 In Proceedings of the ACL
, 2004
"... Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as “thumbs up” or “thumbs down”. To determine this sentiment polarity, we propose a novel machinelearning method that applies textcategorization techniques to just the ..."
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Cited by 589 (7 self)
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the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of crosssentence contextual constraints. Publication info: Proceedings of the ACL, 2004. 1
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
A Framework for Dynamic Graph Drawing
 CONGRESSUS NUMERANTIUM
, 1992
"... Drawing graphs is an important problem that combines flavors of computational geometry and graph theory. Applications can be found in a variety of areas including circuit layout, network management, software engineering, and graphics. The main contributions of this paper can be summarized as follows ..."
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Cited by 627 (44 self)
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as follows: ffl We devise a model for dynamic graph algorithms, based on performing queries and updates on an implicit representation of the drawing, and we show its applications. ffl We present several efficient dynamic drawing algorithms for trees, seriesparallel digraphs, planar stdigraphs, and planar
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
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