Pavel Braslavski
- Artificial Intelligence top 10%
- Information Systems top 10%
- Social Psychology
- Computer Vision and Pattern Recognition
- Sociology and Political Science
- Co-authors
- Eugene AgichteinDenis SavenkovDmitry UstalovDmitry I. IgnatovYana VolkovichMatthias HagenBenno SteinGeorg Rehm
- Topics
- Topic Modeling (11 papers)Natural Language Processing Techniques (11 papers)Advanced Text Analysis Techniques (8 papers)
- Partner nations
- RussiaNetherlandsGermany
In The Last Decade
Pavel Braslavski
37 papers receiving 230 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 180
- Information Systems 80
- Social Psychology 32
- Computer Vision and Pattern Recognition 20
- Sociology and Political Science 17
Countries citing papers authored by Pavel Braslavski
This map shows the geographic impact of Pavel Braslavski'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 Braslavski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavel Braslavski more than expected).
Fields of papers citing papers by Pavel Braslavski
This network shows the impact of papers produced by Pavel Braslavski. 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 Braslavski. The network helps show where Pavel Braslavski may publish in the future.
Co-authorship network of co-authors of Pavel Braslavski
This figure shows the co-authorship network connecting the top 25 collaborators of Pavel Braslavski. A scholar is included among the top collaborators of Pavel Braslavski 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 Braslavski. Pavel Braslavski is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 6 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 4 | |
| 10 | 13 | |
| 11 | 8 | |
| 12 | 20 | |
| 13 | 17 | |
| 14 | 5 | |
| 15 | 4 | |
| 16 | 2 | |
| 17 | 3 | |
| 18 | Towards a Reference Corpus of Web Genres for the Evaluation of Genre Identification Systems | 27 |
| 19 | 0 | |
| 20 | Experiment on Style-Dependent Document Ranking * | 1 |
About Pavel Braslavski
Pavel Braslavski is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research, having authored 43 papers that have together received 260 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Natural Language Processing Techniques (11 papers) and Advanced Text Analysis Techniques (8 papers). The work is most often cited by research in Artificial Intelligence (180 citations), Information Systems (80 citations) and Computer Science Applications (16 citations). Pavel Braslavski has collaborated with scholars based in Russia, Netherlands and Germany. Frequent co-authors include Eugene Agichtein, Denis Savenkov, Dmitry Ustalov, Dmitry I. Ignatov, Yana Volkovich, Matthias Hagen, Benno Stein, Georg Rehm, Ilya Markov and Alexander Mehler. Their work appears in journals such as Bioinformatics, IEEE Access and Lecture notes in computer science.
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.