Vladimir Ejov

435 total citations
28 papers, 272 citations indexed

About

Vladimir Ejov is a scholar working on Computational Theory and Mathematics, Geometry and Topology and Statistics and Probability. According to data from OpenAlex, Vladimir Ejov has authored 28 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 9 papers in Geometry and Topology and 8 papers in Statistics and Probability. Recurrent topics in Vladimir Ejov's work include Graph theory and applications (9 papers), Advanced Graph Theory Research (8 papers) and Markov Chains and Monte Carlo Methods (8 papers). Vladimir Ejov is often cited by papers focused on Graph theory and applications (9 papers), Advanced Graph Theory Research (8 papers) and Markov Chains and Monte Carlo Methods (8 papers). Vladimir Ejov collaborates with scholars based in Australia, Netherlands and France. Vladimir Ejov's co-authors include Jerzy A. Filar, Kate Smith‐Miles, Mehdi Foumani, Vivek S. Borkar, Nelly Litvak, Konstantin Avrachenkov, Stephen K. Lucas, Charles Pearce, Adam J. Blanch and Christopher T. Gibson and has published in prestigious journals such as Automatica, European Journal of Operational Research and Nanotechnology.

In The Last Decade

Vladimir Ejov

27 papers receiving 266 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Vladimir Ejov Australia 10 64 52 49 49 42 28 272
Charles Bordenave France 17 29 0.5× 44 0.8× 84 1.7× 91 1.9× 261 6.2× 56 709
Ján Lang United States 15 79 1.2× 190 3.7× 52 1.1× 15 0.3× 30 0.7× 74 696
Vladimir Boginski United States 4 16 0.3× 58 1.1× 12 0.2× 91 1.9× 13 0.3× 6 283
Κωνσταντίνος Παναγιώτου Germany 14 10 0.2× 109 2.1× 38 0.8× 57 1.2× 26 0.6× 61 482
Tobias Müller Netherlands 10 35 0.5× 175 3.4× 41 0.8× 33 0.7× 22 0.5× 59 361
Anant P. Godbole United States 12 12 0.2× 117 2.3× 37 0.8× 14 0.3× 137 3.3× 58 451
Richard Johnsonbaugh United States 10 15 0.2× 107 2.1× 21 0.4× 7 0.1× 10 0.2× 30 277
Yatsuka Nakamura Japan 9 5 0.1× 127 2.4× 101 2.1× 41 0.8× 24 0.6× 61 351
Stavros Papastavridis Greece 17 12 0.2× 61 1.2× 26 0.5× 15 0.3× 372 8.9× 61 803
William Karush United States 11 68 1.1× 176 3.4× 14 0.3× 25 0.5× 16 0.4× 29 468

Countries citing papers authored by Vladimir Ejov

Since Specialization
Citations

This map shows the geographic impact of Vladimir Ejov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vladimir Ejov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir Ejov more than expected).

Fields of papers citing papers by Vladimir Ejov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vladimir Ejov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vladimir Ejov. The network helps show where Vladimir Ejov may publish in the future.

Co-authorship network of co-authors of Vladimir Ejov

This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Ejov. A scholar is included among the top collaborators of Vladimir Ejov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vladimir Ejov. Vladimir Ejov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Foumani, Mehdi, et al.. (2020). A transformation technique for the clustered generalized traveling salesman problem with applications to logistics. European Journal of Operational Research. 285(2). 444–457. 97 indexed citations
2.
Ejov, Vladimir, et al.. (2017). A note on using the resistance-distance matrix to solve Hamiltonian cycle problem. Annals of Operations Research. 261(1-2). 393–399. 1 indexed citations
3.
Ejov, Vladimir, et al.. (2015). A linear-size conversion of HCP to 3HCP. The Australasian Journal of Combinatorics. 62(1). 45–58. 1 indexed citations
4.
Slattery, Ashley D., Adam J. Blanch, Vladimir Ejov, Jamie S. Quinton, & Christopher T. Gibson. (2014). Spring constant calibration techniques for next-generation fast-scanning atomic force microscope cantilevers. Nanotechnology. 25(33). 335705–335705. 22 indexed citations
5.
Ejov, Vladimir, et al.. (2012). Incompetence and impact of training in bimatrix games. Automatica. 48(10). 2400–2408. 5 indexed citations
6.
Ejov, Vladimir, et al.. (2011). Proof of the Hamiltonicity-Trace Conjecture for Singularly Perturbed Markov Chains. Journal of Applied Probability. 48(4). 901–910. 3 indexed citations
7.
Ejov, Vladimir, et al.. (2011). Proof of the Hamiltonicity-Trace Conjecture for Singularly Perturbed Markov Chains. Journal of Applied Probability. 48(4). 901–910. 1 indexed citations
8.
Avrachenkov, Konstantin, Vladimir Ejov, & Jerzy A. Filar. (2010). Multivariate polynomial perturbations of algebraic equations. Journal of Mathematical Analysis and Applications. 369(1). 214–221. 1 indexed citations
9.
Ejov, Vladimir, et al.. (2009). A note on the graph’s resolvent and the multifilar structure. Linear Algebra and its Applications. 431(8). 1367–1379. 6 indexed citations
10.
Ejov, Vladimir, et al.. (2009). Consistent behavior of certain perturbed determinants induced by graphs. Linear Algebra and its Applications. 431(5-7). 543–552. 3 indexed citations
11.
Litvak, Nelly & Vladimir Ejov. (2009). Markov Chains and Optimality of the Hamiltonian Cycle. Mathematics of Operations Research. 34(1). 71–82. 9 indexed citations
12.
Ejov, Vladimir, et al.. (2009). Refined MDP-Based Branch-and-Fix Algorithm for the Hamiltonian Cycle Problem. Mathematics of Operations Research. 34(3). 758–768. 8 indexed citations
13.
Howlett, Phil, Konstantin Avrachenkov, Charles Pearce, & Vladimir Ejov. (2008). Inversion of analytically perturbed linear operators that are singular at the origin. Journal of Mathematical Analysis and Applications. 353(1). 68–84. 15 indexed citations
14.
Borkar, Vivek S., Vladimir Ejov, & Jerzy A. Filar. (2008). On the Hamiltonicity Gap and doubly stochastic matrices. Random Structures and Algorithms. 34(4). 502–519. 8 indexed citations
15.
Ejov, Vladimir, Jerzy A. Filar, & F. M. Spieksma. (2007). On regularly perturbed fundamental matrices. Journal of Mathematical Analysis and Applications. 336(1). 18–30.
16.
Ejov, Vladimir, Jerzy A. Filar, Stephen K. Lucas, & Peter Zograf. (2006). Clustering of spectra and fractals of regular graphs. Journal of Mathematical Analysis and Applications. 333(1). 236–246. 12 indexed citations
17.
Ejov, Vladimir, Jerzy A. Filar, Stephen K. Lucas, & Jelani Nelson. (2006). SOLVING THE HAMILTONIAN CYCLE PROBLEM USING SYMBOLIC DETERMINANTS. Taiwanese Journal of Mathematics. 10(2). 8 indexed citations
18.
Howlett, Phil, Vladimir Ejov, & Konstantin Avrachenkov. (2004). Inversion of perturbed linear operators that are singular at the origin. 6. 5628–5631. 2 indexed citations
19.
Borkar, Vivek S., Vladimir Ejov, & Jerzy A. Filar. (2004). Directed graphs, Hamiltonicity and doubly stochastic matrices. Random Structures and Algorithms. 25(4). 376–395. 9 indexed citations
20.
Ejov, Vladimir, et al.. (2003). Geometric interpretation of Hamiltonian cycles problem via singularly perturbed Markov decision processes. Optimization. 52(4-5). 441–458. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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