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Distributed randomized algorithms for the PageRank computation
 IEEE Trans. Autom. Control
, 1987
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A survey on distributed approaches to graph based reputation measures
, 2007
"... Reputation systems are indispensable for the operation of Internet mediated services, electronic markets, document ranking systems, P2P networks and Ad Hoc networks. Here we survey available distributed approaches to the graph based reputation measures. Graph based reputation measures can be viewed ..."
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Reputation systems are indispensable for the operation of Internet mediated services, electronic markets, document ranking systems, P2P networks and Ad Hoc networks. Here we survey available distributed approaches to the graph based reputation measures. Graph based reputation measures can be viewed as random walks on directed weighted graphs whose edges represent interactions among peers. We classify the distributed approaches to graph based reputation measures into three categories. The first category is based on asynchronous methods. The second category is based on the aggregation/decomposition methods. And the third category is based on the personalization methods which use local information.
SiteBased Partitioning and Repartitioning Techniques for Parallel PageRank Computation
"... Abstract—The PageRank algorithm is an important component in effective web search. At the core of this algorithm are repeated sparse matrixvector multiplications where the involved web matrices grow in parallel with the growth of the web and are stored in a distributed manner due to space limitatio ..."
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Abstract—The PageRank algorithm is an important component in effective web search. At the core of this algorithm are repeated sparse matrixvector multiplications where the involved web matrices grow in parallel with the growth of the web and are stored in a distributed manner due to space limitations. Hence, the PageRank computation, which is frequently repeated, must be performed in parallel with highefficiency and lowpreprocessing overhead while considering the initial distributed nature of the web matrices. Our contributions in this work are twofold. We first investigate the application of stateoftheart sparse matrix partitioning models in order to attain high efficiency in parallel PageRank computations with a particular focus on reducing the preprocessing overhead they introduce. For this purpose, we evaluate two different compression schemes on the web matrix using the site information inherently available in links. Second, we consider the more realistic scenario of starting with an initially distributed data and extend our algorithms to cover the repartitioning of such data for efficient PageRank computation. We report performance results using our parallelization of a stateoftheart PageRank algorithm on two different PC clusters with 40 and 64 processors. Experiments show that the proposed techniques achieve considerably high speedups while incurring a preprocessing overhead of several iterations (for some instances even less than a single iteration) of the underlying sequential PageRank algorithm. Index Terms—PageRank, sparse matrixvector multiplication, web search, parallelization, sparse matrix partitioning, graph partitioning, hypergraph partitioning, repartitioning. Ç
Maiter: An Asynchronous Graph Processing Framework for Deltabased Accumulative Iterative Computation
"... Myriad of graphbased algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a largescale distributed environment in order to scale to massive data sets. To accelerate these largescale graphbased iterative computations, we ..."
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Myriad of graphbased algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a largescale distributed environment in order to scale to massive data sets. To accelerate these largescale graphbased iterative computations, we propose deltabased accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the “changes” between iterations. By DAIC, we can process only the “changes” to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the highcost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other stateoftheart frameworks.
unknown title
, 2007
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
AN INNEROUTER ITERATION FOR COMPUTING PAGERANK
"... Abstract. We present a new iterative scheme for PageRank computation. The algorithm is applied to the linear system formulation of the problem, using innerouter stationary iterations. It is simple, can be easily implemented and parallelized, and requires minimal storage overhead. Our convergence an ..."
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Abstract. We present a new iterative scheme for PageRank computation. The algorithm is applied to the linear system formulation of the problem, using innerouter stationary iterations. It is simple, can be easily implemented and parallelized, and requires minimal storage overhead. Our convergence analysis shows that the algorithm is effective for a crude inner tolerance and is not sensitive to the choice of the parameters involved. The same idea can be used as a preconditioning technique for nonstationary schemes. Numerical examples featuring matrices of dimensions exceeding 100,000,000 in sequential and parallel environments demonstrate the merits of our technique. Our code is available online for viewing and testing, along with several large scale examples.
PageRank: Splitting Homogeneous Singular Linear Systems of Index One
"... Abstract. The PageRank algorithm is used today within web information retrieval to provide a contentneutral ranking metric over web pages. It employs power method iterations to solve for the steadystate vector of a DTMC. The defining onestep probability transition matrix of this DTMC is derived f ..."
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Abstract. The PageRank algorithm is used today within web information retrieval to provide a contentneutral ranking metric over web pages. It employs power method iterations to solve for the steadystate vector of a DTMC. The defining onestep probability transition matrix of this DTMC is derived from the hyperlink structure of the web and a model of web surfing behaviour which accounts for user bookmarks and memorised URLs. In this paper we look to provide a more accessible, more broadly applicable explanation than has been given in the literature of how to make PageRank calculation more tractable through removal of the danglingpage matrix. This allows web pages without outgoing links to be removed before we employ power method iterations. It also allows decomposition of the problem according to irreducible subcomponents of the original transition matrix. Our explanation also covers a PageRank extension to accommodate TrustRank. In setting out our alternative explanation, we introduce and apply a general linear algebraic theorem which allows us to map homogeneous singular linear systems of index one to inhomogeneous nonsingular linear systems with a shared solution vector. As an aside, we show in this paper that irreducibility is not required for PageRank to be welldefined. 1