Polykarpos Meladianos
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 10%
- Computer Vision and Pattern Recognition
- Molecular Biology
- Information Systems
- Co-authors
- Michalis VazirgiannisGiannis NikolentzosAntoine J.‐P. TixierJean-Pierre LorréYannis StavrakasFrançois RousseauZekun ZhangDionysios Kehagias
- Topics
- Advanced Text Analysis Techniques (7 papers)Topic Modeling (6 papers)Advanced Graph Neural Networks (3 papers)
- Cited by
- Artificial IntelligenceStatistical and Nonlinear PhysicsComputer Vision and Pattern Recognition
- Journals
- arXiv (Cornell University)Proceedings of the AAAI Conference on Artificial IntelligenceText REtrieval Conference
In The Last Decade
Polykarpos Meladianos
11 papers receiving 247 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 213
- Statistical and Nonlinear Physics 68
- Computer Vision and Pattern Recognition 53
- Molecular Biology 39
- Information Systems 22
Countries citing papers authored by Polykarpos Meladianos
This map shows the geographic impact of Polykarpos Meladianos'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 Polykarpos Meladianos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Polykarpos Meladianos more than expected).
Fields of papers citing papers by Polykarpos Meladianos
This network shows the impact of papers produced by Polykarpos Meladianos. 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 Polykarpos Meladianos. The network helps show where Polykarpos Meladianos may publish in the future.
Co-authorship network of co-authors of Polykarpos Meladianos
This figure shows the co-authorship network connecting the top 25 collaborators of Polykarpos Meladianos. A scholar is included among the top collaborators of Polykarpos Meladianos 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 Polykarpos Meladianos. Polykarpos Meladianos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 60 | |
| 3 | 24 | |
| 4 | Classifying Graphs as Images with Convolutional Neural Networks. | 5 |
| 5 | 18 | |
| 6 | 23 | |
| 7 | 91 | |
| 8 | 16 | |
| 9 | 9 | |
| 10 | AUEB at TREC 2015: Clinical Decision Support Track. | 2 |
| 11 | 6 |
About Polykarpos Meladianos
Polykarpos Meladianos is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Transportation, having authored 11 papers that have together received 260 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (7 papers), Topic Modeling (6 papers) and Advanced Graph Neural Networks (3 papers). The work is most often cited by research in Artificial Intelligence (213 citations), Statistical and Nonlinear Physics (68 citations) and Computer Vision and Pattern Recognition (53 citations). Polykarpos Meladianos has collaborated with scholars based in France, Greece and Canada. Frequent co-authors include Michalis Vazirgiannis, Giannis Nikolentzos, Antoine J.‐P. Tixier, Jean-Pierre Lorré, Yannis Stavrakas, François Rousseau, Zekun Zhang, Dionysios Kehagias and Dimitrios Tzovaras. Their work appears in journals such as arXiv (Cornell University), Proceedings of the AAAI Conference on Artificial Intelligence and Text REtrieval Conference.
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.