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The effect of new links on Google PageRank
 Stoch. Models
, 2006
"... PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as the frequency that a random surfer visits a Web page, and thus it reflects the popularity of a Web page. We study the effect of newly created links on Google PageRank. We discuss to wh ..."
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Cited by 7 (5 self)
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PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as the frequency that a random surfer visits a Web page, and thus it reflects the popularity of a Web page. We study the effect of newly created links on Google PageRank. We discuss to what extent a page can control its PageRank. Using the asymptotic analysis we provide simple conditions that show whether or not new links result in increased PageRank for a Web page and its neighbors. Furthermore, we show that there exists an optimal (although impractical) linking strategy. We conclude that a Web page benefits from links inside its Web community and on the other hand irrelevant links penalize the Web pages and their Web communities.
Parallel Algorithms for Hypergraph Partitioning
, 2006
"... Nearoptimal decomposition is central to the efficient solution of numerous problems in scientific computing and computeraided design. In particular, intelligent a priori partitioning of input data can greatly improve the runtime and scalability of largescale parallel computations. Discrete data ..."
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Cited by 4 (1 self)
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Nearoptimal decomposition is central to the efficient solution of numerous problems in scientific computing and computeraided design. In particular, intelligent a priori partitioning of input data can greatly improve the runtime and scalability of largescale parallel computations. Discrete data structures such as graphs and hypergraphs are used to formalise such partitioning problems, with hypergraphs typically preferred for their greater expressiveness. Optimal graph and hypergraph partitioning are NPcomplete problems; however, serial heuristic algorithms that run in loworder polynomial time have been studied extensively and good tool support exists. Yet, to date, only graph partitioning algorithms have been parallelised. This thesis presents the first parallel hypergraph partitioning algorithms, enabling both partitioning of much larger hypergraphs, and computation of partitions with significantly reduced runtimes. In the multilevel approach which we adopt, the coarsening and refinement phases are performed in parallel while the initial
WebSiteBased Partitioning Techniques for Reducing the Preprocessing Overhead before the Parallel PageRank Computations
"... Abstract. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. Due to the enormous size of the Web’s hyperlink structure, PageRank computations are usually carried out on parallel computers. Recen ..."
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Cited by 2 (1 self)
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Abstract. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. Due to the enormous size of the Web’s hyperlink structure, PageRank computations are usually carried out on parallel computers. Recently, a hypergraphpartitioningbased formulation for parallel sparsematrix vector multiplication is proposed as a preprocessing step which will minimize the communication overhead of the parallel PageRank computations. Based on this work, we propose Websitebased partitioning approaches in order to reduce the overhead of this preprocessing step. The conducted experiments show that the proposed approach produces comparable performance results for PageRank computation while achieving lower preprocessing overheads. 1
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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Cited by 1 (1 self)
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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
Generating Msg’s by Binrank for Scaling in Dynamic Authority Based Search 1
"... BinRank is a system that approximates object rank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. Number of relatively small subsets of the data graph are materialized in such a way that any keyword query can be answered by running ObjectRank on ..."
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BinRank is a system that approximates object rank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. Number of relatively small subsets of the data graph are materialized in such a way that any keyword query can be answered by running ObjectRank on only one of the subgraphs. BinRank generates the subgraphs by partitioning all the terms in the corpus based on their cooccurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive nonnegligible scores. The intuition is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing highquality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 108 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authoritybased search systems have been able to demonstrate. Experimental evaluation investigates the tradeoff between query execution time, quality of the results, and storage requirements of BinRank.
A REVIEW ON: DYNAMIC LINK BASED RANKING
"... Dynamic authoritybased ranking methods such as personalized PageRank and ObjectRank. Since they dynamically rank nodes in a data graph using an expensive matrixmultiplication method, the online execution time rapidly increases as the size of data graph grows. ObjectRank spends 2040 seconds to com ..."
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Dynamic authoritybased ranking methods such as personalized PageRank and ObjectRank. Since they dynamically rank nodes in a data graph using an expensive matrixmultiplication method, the online execution time rapidly increases as the size of data graph grows. ObjectRank spends 2040 seconds to compute queryspecific relevance scores, which is unacceptable. We introduce a novel approach, BinRank, that approximates dynamic linkbased ranking scores efficiently. BinRank partitions a dictionary into bins of relevant keywords and then constructs materialized subgraphs (MSGs) per bin in preprocessing stage. In query time, to produce