By Ravi Kumar, D Sivakumar
This e-book constitutes the refereed lawsuits of the seventh foreign 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 awarded have been rigorously reviewed and chosen from 19 submissions.
Read or Download Algorithms and Models for the Web-Graph: 7th International Workshop, WAW 2010, Stanford, CA, USA, December 13-14, 2010, Proceedings PDF
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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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The process is algorithmically defined as follows. Auxiliary Process. Start from an arbitrary node v0 ∈ V . Denote by Vt the cumulative set of nodes visited by time t, which we initialize to V0 = {v0 }. Denote the cumulative set of all attributes [6] associated with the set Vt by t Wt = W (vτ ). (6) τ =0 Now consider the set of nodes adjacent to Vt but not yet visited: v ∈ V \ Vt : W (v) ∩ Wt = ∅ . (7) 40 M. Bradonji´c et al. Following [2], we call this the set of alive nodes at time t. Unlike in [2], however, we do not keep track of the actual list of alive nodes, but only the size of the set, which we denote by the random variable v ∈ V \ Vt : W (v) ∩ Wt = ∅ Yt = .
601–623. North-Holland, Amsterdam (1970) 14. : Finding a minimum circuit in a graph. In: STOC (1977) 15. : Extensions of Lipschitz mappings into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984) 16. : New Streaming Algorithms for Counting Triangles in Graphs. In: Wang, L. ) COCOON 2005. LNCS, vol. 3595, pp. 710–716. Springer, Heidelberg (2005) 17. : Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations. In: SIAM Data Mining, SDM 2010 (2010) 18. : PEGASUS: A Peta-Scale Graph Mining System.
Counting triangles in large graphs using randomized matrix trace estimation. In: Proceedings of KDD-LDMTA 2010 (2010) 3. : Reductions in streaming algorithms, with an application to counting triangles in graphs. In: SODA (2002) 4. : Efficient Semi-Streaming Algorithms for Local Triangle Counting in Massive Graphs. In: KDD (2008) 5. : Counting Triangles in Data Streams. In: PODS (2006) 6. : A Note on an Inequality Involving the Normal Distribution. Annals of Probability 9(3), 533–535 (1981) 7.