Tomasz Kajdanowicz
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 5%
- Sociology and Political Science
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Przemysław KazienkoPiotr SzymańskiKatarzyna MusiałNitesh V. ChawlaMikołaj MorzyPiotr BródkaRadosław MichalskiMarcin Gruza
- Topics
- Complex Network Analysis Techniques (19 papers)Text and Document Classification Technologies (10 papers)Advanced Graph Neural Networks (8 papers)
- Partner nations
- PolandUnited KingdomUnited States
In The Last Decade
Tomasz Kajdanowicz
49 papers receiving 826 citations
Peers
Comparison fields: 5 of 137
- Artificial Intelligence 424
- Statistical and Nonlinear Physics 196
- Information Systems 189
- Sociology and Political Science 104
- Computer Vision and Pattern Recognition 97
Countries citing papers authored by Tomasz Kajdanowicz
This map shows the geographic impact of Tomasz Kajdanowicz'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 Tomasz Kajdanowicz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomasz Kajdanowicz more than expected).
Fields of papers citing papers by Tomasz Kajdanowicz
This network shows the impact of papers produced by Tomasz Kajdanowicz. 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 Tomasz Kajdanowicz. The network helps show where Tomasz Kajdanowicz may publish in the future.
Co-authorship network of co-authors of Tomasz Kajdanowicz
This figure shows the co-authorship network connecting the top 25 collaborators of Tomasz Kajdanowicz. A scholar is included among the top collaborators of Tomasz Kajdanowicz 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 Tomasz Kajdanowicz. Tomasz Kajdanowicz 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 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 68 | |
| 7 | 8 | |
| 8 | 18 | |
| 9 | UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree | 1 |
| 10 | 42 | |
| 11 | scikit-multilearn: A Python library for Multi-Label Classification | 12 |
| 12 | 28 | |
| 13 | 17 | |
| 14 | 32 | |
| 15 | A Network Perspective on Stratification of Multi-Label Data | 5 |
| 16 | 0 | |
| 17 | 8 | |
| 18 | 10 | |
| 19 | PRIVACY-PRESERVING DATA MINING, SHARING AND PUBLISHING | 4 |
| 20 | 96 |
About Tomasz Kajdanowicz
Tomasz Kajdanowicz is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Information Systems, having authored 54 papers that have together received 863 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (19 papers), Text and Document Classification Technologies (10 papers) and Advanced Graph Neural Networks (8 papers). The work is most often cited by research in Statistical and Nonlinear Physics (196 citations), Artificial Intelligence (424 citations) and Information Systems (189 citations). Tomasz Kajdanowicz has collaborated with scholars based in Poland, United Kingdom and United States. Frequent co-authors include Przemysław Kazienko, Piotr Szymański, Katarzyna Musiał, Nitesh V. Chawla, Mikołaj Morzy, Piotr Bródka, Radosław Michalski, Marcin Gruza, Jan Kocoń and Kristian Kersting. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.
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.