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 ebook constitutes the lawsuits of the thirteenth foreign Workshop on Algorithms and versions for the internet Graph, WAW 2016, held in Montreal, quality control, Canada, in December 2016.
The thirteen complete papers provided during this quantity have been conscientiously reviewed and chosen from 14 submissions. The workshop amassed the researchers who're engaged on graph-theoretic and algorithmic points of similar advanced networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks coming up from the Internet.

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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

Example text

Label propagation and quadratic criterion. In: Semi-supervised Learning, ch. 10 (2006) 5. : Parallel and Distributed Computation: Numerical Methods. Prentice Hall, Englewood Cliffs (1989) 6. : Stochastic Approximation: A Dynamical Systems Viewpoint. Hindustan Publishing Agency, Cambridge University Press, New Delhi, Cambridge (2008) 7. : Randomized Kaczmarz for rank aggregation from pairwise comparisons. In: IEEE ITW (2016) 8. : Semi-supervised Learning. MIT Press, London (2006) 9. : The matrix-forest theorem and measuring relations in small social groups.

Hii = j Bij Define P as P = H −1 B, viewed as the transition probability matrix on the graph, and Q is its irreducible counterpart as in the PageRank algorithm: Q = (1 − ) P + /N E, where E is an N × N matrix with all 1’s. Let Xt , t ≥ 0, be a Markov chain with transition matrix Q and {ηt }t≥0 a positive step-size sequence satisfying t≥0 ηt = ∞ and 2 t≥0 ηt < ∞. The stochastic approximation scheme to solve (3) is: =xti + ηt I{Xt = i} xt+1 i P (i, Xt+1 ) Hii xtXt+1 − xti + Yi . Q(i, Xt+1 ) (4) Convergence Analysis.

Springer, Heidelberg (2013). 1007/978-3-319-03536-9 15 22. : Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998) 23. : Maximal planar networks with large clustering coefficient and power-law degree distribution. Phys. Rev. fi/~lleskela/ Abstract. We discuss a notion of clustering for directed graphs, which describes how likely two followers of a node are to follow a common target. The associated network motifs, called dicliques or bi-fans, have been found to be key structural components in various real-world networks.

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