Maksim Zhukovskii

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In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges. We propose gradient-based and random gradient-free methods to solve this problem. Our algorithms are based on the concept of an inexact oracle and unlike the state-of-the-art gradient-based method we(More)
In the paper we investigate power law for PageRank components for the Buckley-Osthus model for web graph. We compare different numerical methods for PageRank calculation. With the best method we do a lot of numerical experiments. These experiments confirm the hypothesis about power law. At the end we discuss real model of web-ranking based on the classical(More)
Let C be a class of graphs and π be a graph parameter. Let Φ be a formula in the first-order language containing only the adjacency and the equality relations. We say that Φ defines C on connected graphs with sufficiently large π if there is a constant k such that, for every connected graph G with π(G) ≥ k, Φ is true on G exactly when G belongs to C. For a(More)
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