Download Algorithms and Models for the Web Graph: 13th International by Anthony Bonato, Fan Chung Graham, Pawel Pralat PDF

By Anthony Bonato, Fan Chung Graham, Pawel Pralat

This e-book constitutes the lawsuits of the thirteenth foreign Workshop on Algorithms and versions for the net Graph, WAW 2016, held in Montreal, quality controls, Canada, in December 2016.
The thirteen complete papers offered during this quantity have been rigorously reviewed and chosen from 14 submissions. The workshop amassed the researchers who're engaged on graph-theoretic and algorithmic facets of similar advanced networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks bobbing up from the Internet.

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Read or Download Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings PDF

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Extra resources for Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings

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9479, pp. 15–28. Springer, Heidelberg (2015). 1007/ 978-3-319-26784-5 2 17. : Dynamics of Large Networks. ProQuest (2008) 18. : Assortative mixing in networks. Phys. Rev. Lett. 89(20), 208701 (2002) 19. : Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(N5), 323–351 (2005) 20. : The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003) Assortativity in Generalized Preferential Attachment Models 21 21. : Generalized preferential attachment: tunable power-law degree distribution and clustering coefficient.

The optimization problem associated with the above stated requirements is to minimize K K T F∗k AF∗k + μ Q(F ) = k=1 (F∗k − Y∗k )T B(F∗k − Y∗k ), (1) k=1 where A is the positive (semi-)definite graph kernel and B the cost of deviation from the labels. Typically, the support of matrix A coincides with the support of the adjacency matrix A and matrix B is diagonal. μ > 0 is the regularization parameter. Majority of all existing graph-based semi-supervised learning methods can be cast into the optimization formulation (1).

Avrachenkov et al. RK-approach implementation of the three methods. The convergence is fastest for the normalized Laplacian while the error is less for regularized Laplacian and harmonic functions methods. Figure 2b shows the comparison of RK-approach, SA-approach, distributed Jacobi and power iteration for normalized Laplacian. In this example, RK and Jacobi show similar behaviour. We recall again that RK and Jacobi use more information and computationally more expensive than SA. 3 Gaussian Mixture Model Graph A Gaussian mixture model graph of 10000 nodes with 3 classes was created with the probability of a node belonging to either of the three classes being equal.

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