Get Algorithms and Models for the Web-Graph: 7th International PDF

By Ravi Kumar, D Sivakumar

ISBN-10: 3642180086

ISBN-13: 9783642180088

This booklet constitutes the refereed complaints of the seventh overseas Workshop on Algorithms and types for the Web-Graph, WAW 2010, held in Stanford, CA, united states, in December 2010, which was once co-located with the sixth overseas Workshop on web and community Economics (WINE 2010). The thirteen revised complete papers and the invited paper provided have been conscientiously reviewed and chosen from 19 submissions.

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Additional info for Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings

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Eng. Inf. Sci. 23(4), 661–674 (2009) 9. : The representation of a graph by set intersections. Canad. J. Math. 18, 106–112 (1966) 10. : Random intersection graphs and classification. In: Advances in Data Analysis, vol. 45, pp. 67–74 (2007) 11. : Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180– 184 (2004) 12. : Random intersection graphs when m = ω(n): An equivalence theorem relating the evolution of the g(n, m, p) and g(n, p) models. Random Struct. Algorithms 16(2), 156–176 (2000) 13.

3503, pp. 606–609. Springer, Heidelberg (2005) 28. : Approximating Clustering Coefficient and Transitivity. Journal of Graph Algorithms and Applications 9, 265–275 (2005) 29. : Fast Counting of Triangles in Large Real Networks, without counting: Algorithms and Laws. In: ICDM (2008) 30. : Counting Triangles Using Projections. KAIS Journal (2010) 31. : Doulion: Counting Triangles in Massive Graphs with a Coin. In: KDD (2009) 32. 3761 33. : Spectral Counting of Triangles via Element-Wise Sparsification and Triangle-Based Link Recommendation.

For t = K0 log n, where K0 is a constant large enough and independent on the initial node v0 , the Chernoff bound ensures that P[Bin(n, (1 − ν)t/n) ≥ t] = o(1/n), and I(Γw ≤t) qw ≤ 1− P{1 − φt−1 ≥ (1 − ν)t/n} = P w∈W =P log 1 1 − pw I(Γw ≤ t) ≥ − log 1 − log 1 1 − pw I(Γw ≤ t) ≥ w∈W ≤P w∈W (1 − ν)t n (1 − ν)t n (1 − ν)t n (22) . Define the auxiliary random variables Xt,w = n log(1/(1 − pw ))I(Γw ≤ t), so that E[Xt,w ] = n log 1 t (1 − qw ) = n pw + o(pw ) 1 − pw 1 − (1 − pw )t tpw + o(tpw ) = ntp2w + o ntp2w , = n pw + o(pw ) (23) which implies p2w 1 + o(1) .

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Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings by Ravi Kumar, D Sivakumar


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