Polina Rozenshtein
- Artificial Intelligence top 10%
- Statistical and Nonlinear Physics top 5%
- Signal Processing top 10%
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Co-authors
- Aristides GionisNikolaj TattiAris AnagnostopoulosB. Aditya PrakashJilles VreekenMauro SozioFrancesco BonchiSee-Kiong Ng
- Topics
- Complex Network Analysis Techniques (7 papers)Advanced Graph Neural Networks (6 papers)Opinion Dynamics and Social Influence (3 papers)
- Journals
- Data Mining and Knowledge DiscoveryACM Transactions on Knowledge Discovery from DataarXiv (Cornell University)
In The Last Decade
Polina Rozenshtein
14 papers receiving 207 citations
Peers
Comparison fields: 5 of 42
- Artificial Intelligence 109
- Statistical and Nonlinear Physics 102
- Signal Processing 60
- Computer Networks and Communications 56
- Computer Vision and Pattern Recognition 40
Countries citing papers authored by Polina Rozenshtein
This map shows the geographic impact of Polina Rozenshtein'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 Polina Rozenshtein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Polina Rozenshtein more than expected).
Fields of papers citing papers by Polina Rozenshtein
This network shows the impact of papers produced by Polina Rozenshtein. 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 Polina Rozenshtein. The network helps show where Polina Rozenshtein may publish in the future.
Co-authorship network of co-authors of Polina Rozenshtein
This figure shows the co-authorship network connecting the top 25 collaborators of Polina Rozenshtein. A scholar is included among the top collaborators of Polina Rozenshtein 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 Polina Rozenshtein. Polina Rozenshtein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 5 | |
| 3 | 6 | |
| 4 | 5 | |
| 5 | 8 | |
| 6 | 2 | |
| 7 | 26 | |
| 8 | 18 | |
| 9 | Methods for analyzing temporal networks | 1 |
| 10 | 17 | |
| 11 | 33 | |
| 12 | European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014, Nancy, France, September 15-19, 2014 | 1 |
| 13 | ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA - August 24 - 27, 2014 | 20 |
| 14 | 68 |
About Polina Rozenshtein
Polina Rozenshtein is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Signal Processing, having authored 14 papers that have together received 212 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Advanced Graph Neural Networks (6 papers) and Opinion Dynamics and Social Influence (3 papers). The work is most often cited by research in Computational Mathematics (5 citations), Statistical and Nonlinear Physics (102 citations) and Signal Processing (60 citations). Polina Rozenshtein has collaborated with scholars based in Finland, Singapore and Poland. Frequent co-authors include Aristides Gionis, Nikolaj Tatti, Aris Anagnostopoulos, B. Aditya Prakash, Jilles Vreeken, Mauro Sozio, Francesco Bonchi, See-Kiong Ng, Sujatha Das Gollapalli and Arpita Biswas. Their work appears in journals such as Data Mining and Knowledge Discovery, ACM Transactions on Knowledge Discovery from Data and arXiv (Cornell University).
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.