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

By Anthony Bonato, Fan Chung Graham, Pawel Pralat

This e-book constitutes the lawsuits of the thirteenth overseas Workshop on Algorithms and versions for the internet Graph, WAW 2016, held in Montreal, quality controls, Canada, in December 2016.

The thirteen complete papers provided during this quantity have been conscientiously reviewed and chosen from 14 submissions. The workshop collected the researchers who're engaged on graph-theoretic and algorithmic points of similar complicated networks, together with social networks, quotation networks, organic networks, molecular networks, and other networks coming up from the Internet.

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Additional resources for Algorithms and Models for the Web Graph: 13th International Workshop, WAW 2016, Montreal, QC, Canada, December 14–15, 2016, Proceedings (Lecture Notes in Computer Science)

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Randomized Kaczmarz Approach Another asynchronously distributed approach is to apply the randomized Kaczmarz algorithm (RK-approach) to (2). We solve the linear system (2) of the form Ax = b, where A = (A + AT ) + μ(B + B T ) and b = μ(B + B T )Y . , pi = N1 for uniform sampling). , N }. Let λmin be the smallest nonˆTi a ˆi . From [7], we have that for λmin ∈ negative eigenvalue of the matrix i pi a ∗ (0, 1), x(t) − x → 0 almost surely and E[ x(t) − x∗ 2 ] → 0 exponentially with rate (1 − λmin ) where x∗ is such that Ax∗ = b.

Bootstrap percolation is a growth model inspired by cellular automata. At the initial time t = 0, the bootstrap percolation process starts from an initial random configuration of active vertices on a given graph, and proceeds deterministically so that a node becomes active at time t = 1, 2, . . if sufficiently many of its neighbors are already active at the previous time t−1. In the most basic model, all vertices have the same initial probability of being active in the initial configuration. ) for the initial probability above this threshold, while this is not the case below the threshold.

We may drop the assumption of identical distribution sampling and still retain exponential decay of mean square error as long as λmin remains bounded away from 1 from above for time-varying p. In fact, one may drop the independence assumption as well, replacing it with the above condition on the conditional sampling probabilities given the history. The exponential decay rate of mean square error then is 1 − λ∗ , where λ∗ < 1 is the aforementioned upper bound. For normal matrices, the condition number of a matrix M is given by (M ) , where λmin (M ) and λmax (M ) are its minimum and maxκ(M ) = λλmax min (M ) imum eigenvalues, respectively.

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