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Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
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Cited by 532 (5 self)
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method that solves the optimization problem over the network. The relation of averaging time to the second largest eigenvalue naturally relates it to the mixing time of a random walk with transition probabilities derived from the gossip algorithm. We use this connection to study the performance
Improved algorithms for topic distillation in a hyperlinked environment
 In SIGIR Conference on Research and Development in Information Retrieval
, 1998
"... Abstract This paper addresses the problem of topic distillation on the World Wide Web, namely, given a typical user query to find quality documents related to the query topic. Connectivity analysis has been shown to be useful in identifying high quality pages within a topic specific graph of hyperli ..."
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Cited by 471 (8 self)
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Abstract This paper addresses the problem of topic distillation on the World Wide Web, namely, given a typical user query to find quality documents related to the query topic. Connectivity analysis has been shown to be useful in identifying high quality pages within a topic specific graph
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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with loops (undirected cycles). The algorithm is an exact inference algorithm for singly connected networks the beliefs converge to the cor rect marginals in a number of iterations equal to the diameter of the graph.1 However, as Pearl noted, the same algorithm will not give the correct beliefs for mul
Approximation Algorithms for Connected Dominating Sets
 Algorithmica
, 1996
"... The dominating set problem in graphs asks for a minimum size subset of vertices with the following property: each vertex is required to either be in the dominating set, or adjacent to some node in the dominating set. We focus on the question of finding a connected dominating set of minimum size, whe ..."
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Cited by 366 (9 self)
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, where the graph induced by vertices in the dominating set is required to be connected as well. This problem arises in network testing, as well as in wireless communication. Two polynomial time algorithms that achieve approximation factors of O(H (\Delta)) are presented, where \Delta is the maximum
Complexity of finding embeddings in a ktree
 SIAM JOURNAL OF DISCRETE MATHEMATICS
, 1987
"... A ktree is a graph that can be reduced to the kcomplete graph by a sequence of removals of a degree k vertex with completely connected neighbors. We address the problem of determining whether a graph is a partial graph of a ktree. This problem is motivated by the existence of polynomial time al ..."
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Cited by 386 (1 self)
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algorithms for many combinatorial problems on graphs when the graph is constrained to be a partial ktree for fixed k. These algorithms have practical applications in areas such as reliability, concurrent broadcasting and evaluation of queries in a relational database system. We determine the complexity
Informationtheoretic metric learning
 in NIPS 2006 Workshop on Learning to Compare Examples
, 2007
"... We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem. Spe ..."
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Cited by 359 (15 self)
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We formulate the metric learning problem as that of minimizing the differential relative entropy between two multivariate Gaussians under constraints on the Mahalanobis distance function. Via a surprising equivalence, we show that this problem can be solved as a lowrank kernel learning problem
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptiv ..."
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Cited by 258 (5 self)
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A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy
When trees collide: An approximation algorithm for the generalized Steiner problem on networks
, 1994
"... We give the first approximation algorithm for the generalized network Steiner problem, a problem in network design. An instance consists of a network with linkcosts and, for each pair fi; jg of nodes, an edgeconnectivity requirement r ij . The goal is to find a minimumcost network using the a ..."
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Cited by 249 (38 self)
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We give the first approximation algorithm for the generalized network Steiner problem, a problem in network design. An instance consists of a network with linkcosts and, for each pair fi; jg of nodes, an edgeconnectivity requirement r ij . The goal is to find a minimumcost network using
A finitevolume, incompressible Navierâ€“Stokes model for studies of the ocean on parallel computers.
 J. Geophys. Res.,
, 1997
"... Abstract. The numerical implementation of an ocean model based on the incompressible Navier Stokes equations which is designed for studies of the ocean circulation on horizontal scales less than the depth of the ocean right up to global scale is described. A "pressure correction" method i ..."
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Cited by 293 (32 self)
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field is separated into surface, hydrostatic, and nonhydrostatic components. First, as in hydrostatic models, a twodimensional problem is inverted for the surface pressure which is then made use of in the threedimensional inversion for the nonhydrostatic pressure. Preconditioned conjugate
On constructing minimum spanning trees in kdimensional space and related problems
 SIAM JOURNAL ON COMPUTING
, 1982
"... . The problem of finding a minimum spanning tree connecting n points in a kdimensional space is discussed under three common distance metrics: Euclidean, rectilinear, and L. By employing a subroutine that solves the post office problem, we show that, for fixed k _> 3, such a minimum spanning t ..."
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Cited by 222 (1 self)
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tree can be found in time O(n2a<k)(1og n)la<k)), where a(k) = 2+1). The bound can be improved to O((n log n) 1"8) for points in 3dimensional Euclidean space. We also obtain o(n 2) algorithms for finding a farthest pair in a set of n points and for other related problems.
Results 1  10
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