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Titlebook: Algorithms and Models for the Web Graph; 11th International W Anthony Bonato,Fan Chung Graham,Paweł Prałat Conference proceedings 2014 Spri

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楼主: implicate
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https://doi.org/10.1007/978-3-658-33192-4atic setting where the LiveRank is computed before any query is made, to dynamic settings where the LiveRank can be updated as queries are processed. Our results show that building on the PageRank can lead to efficient LiveRanks for Web graphs.
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Modelling of Trends in Twitter Using Retweet Graph Dynamics,the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.
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0302-9743 ng on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.978-3-319-13122-1978-3-319-13123-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Grundzüge stationärer Rückfallarbeitd with a constant global clustering..In this paper we prove that if the degree distribution obeys the power law with an infinite variance, then the global clustering coefficient tends to zero with high probability as the size of a graph grows.
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Efficient Primal-Dual Graph Algorithms for MapReduce,lying polytope so that the number of iterations becomes small, but an approximate primal solution can still be recovered from the approximate dual solution. We then show an application of the same technique to the fractional maximum matching problem in bipartite graphs. Our results also map naturally to the PRAM model.
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Conference proceedings 2014g, China, in December 2014. .The 12 papers presented were carefully reviewed and selected for inclusion in this volume. The aim of the workshop was to further the understanding of graphs that arise from the Web and various user activities on the Web, and stimulate the development of high-performance
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Clustering and the Hyperbolic Geometry of Complex Networks,s, implementing the assumption that hyperbolic geometry underlies the structure of these networks. We do a rigorous analysis of clustering and characterize the global clustering coefficient in terms of the parameters of the model. We show how the global clustering coefficient can be tuned by these parameters, giving an explicit formula.
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