Download Algorithms and Models for the Web-Graph: 7th International by Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar PDF

By Andrei Broder (auth.), Ravi Kumar, Dandapani Sivakumar (eds.)

This e-book constitutes the refereed court cases 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 used to be co-located with the sixth overseas Workshop on net and community Economics (WINE 2010).

The thirteen revised complete papers and the invited paper offered have been conscientiously reviewed and chosen from 19 submissions.

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Read Online 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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WiK ∈ RK , where Kis the number of different edge types. A composite similarity can be defined by a function RK → R to reduce the weight vector to a single number. In this paper, we will restrict ourselves to linear functions such that the composite edge weight wi (α) K is defined as j=1 αj wij . 1 Clustering in Graphs Intuitively, the goal of clustering is to break down the graph into smaller groups such that vertices in each group are tightly coupled among themselves, and loosely coupled with the remainder of the network.

In this formulation, 4m corresponds to the number of edges between vertices vi and vj in a random graph with the given degree distribution, and its subtraction corresponds to the the null hypothesis. This formulation can be generalized for weighted graphs by redefining eij as the weight of this edge (0 if no such edge exists), di as the cumulative weight of edges incident to vi ; and m as the cumulative weight of all edges in the graph [8]. 3 Solving the Optimization Problems We have presented several nonlinear optimization problems for which the derivative information is not available.

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. In: ASONAM (2010) 34. : On the concentration of multivariate polynomials with small expectation. Random Structures and Algorithms 16(4), 344–363 (2000) 35. : Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences).

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