Danil Nemirovsky
Saint Petersburg State University
11 Papers
261 Citations
Danil Nemirovsky is an academic researcher from Saint Petersburg State University. The author has contributed to research in topics: PageRank & Web page. The author has an hindex of 7, co-authored 11 publications. Previous affiliations of Danil Nemirovsky include French Institute for Research in Computer Science and Automation.
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Papers
Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient
TL;DR: This work proposes and analyzes Monte Carlo-type methods for the PageRank computation and suggests several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method.
•Journal Article
Monte Carlo methods in PageRank computation: When one iteration is sufficient
TL;DR: In this article, the authors proposed and analyzed Monte Carlo type methods for the PageRank computation and found that the Monte Carlo methods provide good estimation of the Page-Rank for relatively important pages already after one iteration.
183
•Proceedings Article
Weighted PageRank: Cluster-Related Weights
Danil Nemirovsky,Konstantin Avrachenkov +1 more
- 01 Nov 2008
TL;DR: Weighted PageRank is proposed where the author is free to weight hyper-links according any possible preferring behaviour of a user, in particular, cluster-related weights are considered.
Monte Carlo methods for top-k personalized PageRank lists and name disambiguation
Konstantin Avrachenkov,Nelly Litvak,Danil Nemirovsky,Elena Smirnova,Marina Sokol +4 more
- 01 Sep 2010
TL;DR: This work proposes Monte Carlo methods for fast detection of top-k Personalized PageRank lists and applies the results to construct efficient algorithms for the person name disambiguation problem.
A survey on distributed approaches to graph based reputation measures
Konstantin Avrachenkov,Danil Nemirovsky,Kim Son Pham +2 more
- 22 Oct 2007
TL;DR: This work surveys available distributed approaches to the graph based reputation measures and classify the distributed approaches into three categories based on asynchronous methods, aggregation/decomposition methods and personalization methods which use the information available locally.