Algorithms and Models for the Web Graph: 9th International - download pdf or read online

By Fan Chung, Alexander Tsiatas (auth.), Anthony Bonato, Jeannette Janssen (eds.)

ISBN-10: 3642305407

ISBN-13: 9783642305405

ISBN-10: 3642305415

ISBN-13: 9783642305412

This e-book constitutes the refereed complaints of the ninth foreign Workshop on Algorithms and types for the Web-Graph, WAW 2012, held in Halifax, Nova Scotia, Canada, in June 2012. The thirteen papers awarded have been conscientiously reviewed and chosen for inclusion during this quantity. They deal with a few themes regarding the complicated networks such hypergraph coloring video games and voter versions; algorithms for detecting nodes with huge levels; random Appolonian networks; and a sublinear set of rules for Pagerank computations.

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Read Online or Download Algorithms and Models for the Web Graph: 9th International Workshop, WAW 2012, Halifax, NS, Canada, June 22-23, 2012. Proceedings PDF

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Additional resources for Algorithms and Models for the Web Graph: 9th International Workshop, WAW 2012, Halifax, NS, Canada, June 22-23, 2012. Proceedings

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Assuming t sufficiently large, and recalling that pA1 < 1, we have EN (v, u, k) ≤ 2A2 k>k∗ k>k∗ ≤ 2A2 (1 + o(1))A2 pe(log v/u + 1/u) k−1 (1 + o(1))A2 e(log v/u + 1/u) C log t k∗ k−1 1 1 − 3A2 /C = O(6−18 log t ) = o(t−4 ). The result follows for u tending to infinity. In the case where u is a constant, it follows from Theorem 1 that a multiplicative correction of e can be used in E(deg− (ti−1 , ti )), leading to an error term of O(t−18 log 2 ) = o(t−4 ), as before. 2 Lower Bound It follows from Theorem 1 that a vertex vi added at time i degree of 1 at time ti = (1 + o(1)) A1 + A2 A2 1 has the expected 1/pA1 i = Θ(i).

The information diffusion model in the blog world. In: Proceedings of the 3rd Workshop on Social Network Mining and Analysis, SNA-KDD 2009, pp. 4:1–4:9. 1731015 9. : Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 497–506. 1557077 10. : Cascading behavior in large blog graphs: Patterns and a model. A Department of Mathematics, Ryerson University, Toronto, ON, Canada, M5B 2K3 Abstract.

Type 1: X = X ∪{v} (LINES 5-6) In this case H and H are the same. Any solution for (t , P , F , D) gives a solution with the same value for (t, P, F , D), as long as the partitions P and P are consistent. That is, P should keep the partitioning of X , and then either throw the new vertex v into one of the elements of the partition, or put it in a new singleton element. Moreover, all solutions for (t, P, F , D) are obtained this way. Hence [see lines 5-6] OPT(t, P, F , D) = min{OPT(t , Q, F , D) : Q ∈ P ∗ v}.

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Algorithms and Models for the Web Graph: 9th International Workshop, WAW 2012, Halifax, NS, Canada, June 22-23, 2012. Proceedings by Fan Chung, Alexander Tsiatas (auth.), Anthony Bonato, Jeannette Janssen (eds.)


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