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Global Mincuts in RNC, and Other Ramifications of a Simple MinCut Algorithm
, 1992
"... This paper presents a new algorithm for nding global mincuts in weighted, undirected graphs. One of the strengths of the algorithm is its extreme simplicity. This randomized algorithm can be implemented as a strongly polynomial sequential algorithm with running time ~ O(mn 2), even if space is res ..."
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Cited by 49 (5 self)
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This paper presents a new algorithm for nding global mincuts in weighted, undirected graphs. One of the strengths of the algorithm is its extreme simplicity. This randomized algorithm can be implemented as a strongly polynomial sequential algorithm with running time ~ O(mn 2), even if space is restricted to O(n), or can be parallelized as an RN C algorithm which runs in time O(log 2 n) on a CRCW PRAM with mn 2 log n processors. In addition to yielding the best known processor bounds on unweighted graphs, this algorithm provides the first proof that the mincut problem for weighted undirected graphs is in RN C. The algorithm does more than find a single mincut; it nds all of them. The algorithm also yields numerous results on network reliability, enumeration of cuts, multiway cuts, and approximate mincuts.
Fast Connected Components Algorithms For The EREW PRAM
 SIAM J. COMPUT
, 1999
"... We present fast and e#cient parallel algorithms for finding the connected components of an undirected graph. These algorithms run on the exclusiveread, exclusivewrite (EREW) PRAM. On a graph with n vertices and m edges, our randomized algorithm runs ..."
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Cited by 26 (3 self)
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We present fast and e#cient parallel algorithms for finding the connected components of an undirected graph. These algorithms run on the exclusiveread, exclusivewrite (EREW) PRAM. On a graph with<F3.492e+05> n<F3.822e+05> vertices and<F3.492e+05> m<F3.822e+05> edges, our randomized algorithm runs in<F3.492e+05><F3.822e+05> O(log<F3.492e+05><F3.822e+05> n) time using<F3.492e+05> (m<F3.822e+05> +<F3.492e+05> n<F2.77e+05><F2.072e+05> 1+#<F3.822e+05><F3.492e+05> )/<F3.822e+05> log<F3.492e+05> n<F3.822e+05> EREW processors (for any fixed<F3.492e+05> # ><F3.822e+05> 0). A variant uses<F3.492e+05> (m<F3.822e+05> +<F3.492e+05><F3.822e+05><F3.492e+05> n)/<F3.822e+05> log<F3.492e+05> n<F3.822e+05> processors and runs in<F3.492e+05><F3.822e+05> O(log<F3.492e+05> n<F3.822e+05> log log<F3.492e+05><F3.822e+05> n) time. A deterministic version of the algorithm runs in<F3.492e+05><F3.822e+05> O(log<F2.77e+05><F2.072e+05><F2.77e+05> 1.5<F3.492e+05><F3.822e+05> n) time using<F3.492e+...
A spectral method to separate disconnected and nearlydisconnected Web graph component
 IN PROC. ACM INT'L CONF KNOWLEDGE DISC. DATA MINING (KDD
, 2001
"... Separation of connected components from a graph with disconnected graph components mostly use breadthfirst search (BFS) or depthfirst search (DFS) graph algorithms. Here we propose a new algebraic method to separate disconnected and nearlydisconnected components. This method is based on spectral ..."
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Cited by 23 (6 self)
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Separation of connected components from a graph with disconnected graph components mostly use breadthfirst search (BFS) or depthfirst search (DFS) graph algorithms. Here we propose a new algebraic method to separate disconnected and nearlydisconnected components. This method is based on spectral graph partitioning, following a key observation that disconnected components will show up, after properly sorted, as stepfunction like curve in the lowest eigenvectors of the Laplacian matrix of the graph. Following an perturbative analysis framework, we systematically analyzed the graph structures, first on the disconnected subgraph case, and second on the effects of adding edges sparsely connecting different subgraphs as a perturbation. Several new results are derived, providing insights to spectral methods and related clustering objective function. Examples are given illustrating the concepts and results our methods. Comparing to the standard graph algorithms, this method has the same O(E + V log(V)) complexity, but is easier to implement (using readily available eigensolvers). Further more the method can easily identify articulation points and bridges on nearlydisconnected graphs. Segmentation of a real example of Web graph for query amazon is given. We found that each disconnected or nearlydisconnected components forms a cluster on a clear topic.
Concurrent Threads and Optimal Parallel Minimum Spanning Trees Algorithm
 J. ACM
, 2001
"... This paper resolves a longstanding open problem on whether the concurrent write capability of parallel random access machine (PRAM) is essential for solving fundamental graph problems like connected components and minimum spanning trees in O(log n) time. Specically, we present a new algorithm to so ..."
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Cited by 16 (1 self)
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This paper resolves a longstanding open problem on whether the concurrent write capability of parallel random access machine (PRAM) is essential for solving fundamental graph problems like connected components and minimum spanning trees in O(log n) time. Specically, we present a new algorithm to solve these problems in O(log n) time using a linear number of processors on the exclusiveread exclusivewrite PRAM. The logarithmic time bound is actually optimal since it is well known that even computing the \OR" of n bits
A linearwork parallel algorithm for finding . . .
, 1994
"... We give the first linearwork parallel algorithm for finding a minimum spanning tree. It is a randomized algorithm, and requires O(2log \Lambda n log n) expected time. It is a modification of the sequential lineartime algorithm of Klein and Tarjan. ..."
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Cited by 14 (1 self)
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We give the first linearwork parallel algorithm for finding a minimum spanning tree. It is a randomized algorithm, and requires O(2log \Lambda n log n) expected time. It is a modification of the sequential lineartime algorithm of Klein and Tarjan.
Implementation of Parallel Graph Algorithms on a Massively Parallel SIMD Computer with Virtual Processing
, 1995
"... We describe our implementation of several PRAM graph algorithms on the massively parallel computer MasPar MP1 with 16,384 processors. Our implementation incorporated virtual processing and we present extensive test data. In a previous project [13], we reported the implementation of a set of paralle ..."
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Cited by 14 (3 self)
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We describe our implementation of several PRAM graph algorithms on the massively parallel computer MasPar MP1 with 16,384 processors. Our implementation incorporated virtual processing and we present extensive test data. In a previous project [13], we reported the implementation of a set of parallel graph algorithms with the constraint that the maximum input size was restricted to be no more than the physical number of processors on the MasPar. The MasPar language MPL that we used for our code does not support virtual processing. In this paper, we describe a method of simulating virtual processors on the MasPar. We recoded and finetuned our earlier parallel graph algorithms to incorporate the usage of virtual processors. Under the current implementation scheme, there is no limit on the number of virtual processors that one can use in the program as long as there is enough main memory to store all the data required during the computation. We also give two general optimization techniq...
An Optimal Randomized Logarithmic Time Connectivity Algorithm for the EREW PRAM
, 1996
"... Improving a long chain of works we obtain a randomised EREW PRAM algorithm for finding the connected components of a graph G = (V; E) with n vertices and m edges in O(logn) time using an optimal number of O((m + n)= log n) processors. The result returned by the algorithm is always correct. The pr ..."
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Cited by 12 (1 self)
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Improving a long chain of works we obtain a randomised EREW PRAM algorithm for finding the connected components of a graph G = (V; E) with n vertices and m edges in O(logn) time using an optimal number of O((m + n)= log n) processors. The result returned by the algorithm is always correct. The probability that the algorithm will not complete in O(log n) time is o(n \Gammac ) for any c ? 0. 1 Introduction Finding the connected components of an undirected graph is perhaps the most basic algorithmic graph problem. While the problem is trivial in the sequential setting, it seems that elaborate methods should be used to solve the problem efficiently in the parallel setting. A considerable number of researchers investigated the complexity of the problem in various parallel models including, in particular, various members of the PRAM family. In this work we consider the EREW PRAM model, the weakest member of this family, and obtain, for the first time, a parallel connectivity algorith...
Finding Connected Components in Graphs
, 1996
"... In this paper, we describe our implementation of several parallel graph algorithms for finding connected components. Our implementation, with virtual processing, is on a 16,384processor MasPar MP1 using the language MPL. We present extensive test data on our code. In our previous projects [21, 22, ..."
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In this paper, we describe our implementation of several parallel graph algorithms for finding connected components. Our implementation, with virtual processing, is on a 16,384processor MasPar MP1 using the language MPL. We present extensive test data on our code. In our previous projects [21, 22, 23], we reported the implementation of an extensible parallel graph algorithms library. We developed general implementation and netuning techniques without expending too much e ort on optimizing each individual routine. We also handled the issue of implementing virtual processing. In this paper, we describe several algorithms and finetuning techniques that we developed for the problem of finding connected components in parallel; many of the finetuning techniques are of general interest, and should be applicable to code for other problems. We present data on the execution time and memory usage of our various implementations.
Parallel Processing Letters, fc World Scienti c Publishing Company SIMPLE AND WORKEFFICIENT PARALLEL ALGORITHMS FOR THE MINIMUM SPANNING TREE PROBLEM
"... Communicated by Two simple and worke cient parallel algorithms for the minimum spanning tree problem are presented. Both algorithms perform O(m log n) work. The rst algorithm runs in O(log2 n) time on an EREW PRAM, while the second algorithm runs in O(log n) time on a Common CRCW PRAM. ..."
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Communicated by Two simple and worke cient parallel algorithms for the minimum spanning tree problem are presented. Both algorithms perform O(m log n) work. The rst algorithm runs in O(log2 n) time on an EREW PRAM, while the second algorithm runs in O(log n) time on a Common CRCW PRAM.