Pavel Berkhin
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 5%
- Computer Networks and Communications top 10%
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- David F. GleichLeonid ZhukovNikhil R. DevanurDragomir YankovRajen SubbaMichael R. EvansWei WuRenzhong Wang
- Topics
- Data Mining Algorithms and Applications (5 papers)Data Management and Algorithms (4 papers)Web Data Mining and Analysis (3 papers)
- Journals
- Internet MathematicsAssociation for Computing Machinery eBooks
- Partner nations
- United StatesUnited KingdomRussia
In The Last Decade
Pavel Berkhin
15 papers receiving 564 citations
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 215
- Statistical and Nonlinear Physics 208
- Information Systems 198
- Computer Networks and Communications 141
- Computer Vision and Pattern Recognition 127
Countries citing papers authored by Pavel Berkhin
This map shows the geographic impact of Pavel Berkhin'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 Pavel Berkhin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavel Berkhin more than expected).
Fields of papers citing papers by Pavel Berkhin
This network shows the impact of papers produced by Pavel Berkhin. 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 Pavel Berkhin. The network helps show where Pavel Berkhin may publish in the future.
Co-authorship network of co-authors of Pavel Berkhin
This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Berkhin. A scholar is included among the top collaborators of Pavel Berkhin 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 Pavel Berkhin. Pavel Berkhin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 3 | |
| 3 | 4 | |
| 4 | 11 | |
| 5 | 10 | |
| 6 | 3 | |
| 7 | 76 | |
| 8 | 4 | |
| 9 | KDD-2007 : proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 12-15, 2007, San Jose, CA, USA | 8 |
| 10 | 86 | |
| 11 | 298 | |
| 12 | Fast Parallel PageRank: A Linear System Approach | 67 |
| 13 | 14 | |
| 14 | 24 | |
| 15 | 14 |
About Pavel Berkhin
Pavel Berkhin is a scholar working on Signal Processing, Information Systems and Marketing, having authored 15 papers that have together received 629 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (5 papers), Data Management and Algorithms (4 papers) and Web Data Mining and Analysis (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (208 citations), Computational Mathematics (9 citations) and Signal Processing (104 citations). Pavel Berkhin has collaborated with scholars based in United States, United Kingdom and Russia. Frequent co-authors include David F. Gleich, Leonid Zhukov, Nikhil R. Devanur, Dragomir Yankov, Rajen Subba, Michael R. Evans, Wei Wu, Wei Wu, Renzhong Wang and Ye Chen. Their work appears in journals such as Internet Mathematics and Association for Computing Machinery eBooks.
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.