Видеолекции и открытые
образовательные материалы Физтеха

Список лекций1244

00:32:12
372
O.Mornev, Institute of Theoretical and Experimental Biophysics The physical mechanisms supporting movement of leading edge of an autowave: the resolution of some paradoxes
00:24:47
359
K.E. Zlobina, O.S. Rukhlenko, G.Th. Guria, Moscow Institute of Physics and Technology, National Research Center for Hematology Mathematical modeling of platelets activation and aging
00:25:42
455
Pavel Sorokin (TISNCM, MISIS, Moscow; MIPT, Dolgoprudny) Simulation of fabrication of two­dimensional transition metal dichalcogenides
00:12:08
426
Pavel Sorokin (TISNUM, Troitsk, Russia) Stiffening of stiffest 2D nanofilms. Computational study
01:03:53
809
Обзор групп Plantae (Растений): Glaucophyta, Chlorophyta, Rhodophyta.
00:33:52
786

Explanation of square planar structure of C_{4}H_{4}. Building of Walsh diagram and interaction diagram for cyclic H_4. A Jahn - Teller distortion.

01:19:35
629
Основные положения и особенности P2M. Управление программами и проектами.
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In this talk I will give an overview of the line of work on mathematical properties of Google PageRank algorithm. We will first discuss computational aspects, and sensitivity to changes in the network. Next, we will zoom in on the remarkable property that the distribution of PageRank in scale-free networks follows a power law with the same exponent as in-degree. We will see how this can be explained by a probabilistic model, based on a stochastic fixed point equation. The main result is the distribution of a family of rankings, which includes Google's PageRank, on a directed configuration model (DCM). The result states that the rank of a randomly chosen node in the graph converges in distribution to a finite random variable that can be written as a linear combination of i.i.d. copies of the endogenous solution to a stochastic fixed point equation. For the first time in the literature, this result establishes a limiting behavior for a complete PageRank distribution. This provides a very accurate approximation for the PageRank distribution on the DCM but also on a complete English Wikipedia graph. The essence of the proof is in coupling of the DCM with a specially constructed tree. The main result is obtained by showing that the ranking in the graph converges with any given precision before the coupling breaks. (joint work with Mariana Olvera-Cravioto and Ningyuan Chen)

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