Panos Kalnis
- Artificial Intelligence top 0.2%
- Computer Networks and Communications top 0.5%
- Signal Processing top 0.5%
- Information Systems top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Co-authors
- Gabriel GhinitaShuo ShangDimitris PapadiasNikos MamoulisChristian S. JensenSpiros SkiadopoulosKian‐Lee TanLisi Chen
- Topics
- Data Management and Algorithms (47 papers)Advanced Database Systems and Queries (25 papers)Graph Theory and Algorithms (19 papers)
- Partner nations
- Saudi ArabiaChinaHong Kong
In The Last Decade
Panos Kalnis
116 papers receiving 5.5k citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 3.2k
- Computer Networks and Communications 1.4k
- Signal Processing 1.4k
- Information Systems 1.1k
- Computer Vision and Pattern Recognition 1.1k
Countries citing papers authored by Panos Kalnis
This map shows the geographic impact of Panos Kalnis'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 Panos Kalnis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Panos Kalnis more than expected).
Fields of papers citing papers by Panos Kalnis
This network shows the impact of papers produced by Panos Kalnis. 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 Panos Kalnis. The network helps show where Panos Kalnis may publish in the future.
Co-authorship network of co-authors of Panos Kalnis
This figure shows the co-authorship network connecting the top 25 collaborators of Panos Kalnis. A scholar is included among the top collaborators of Panos Kalnis 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 Panos Kalnis. Panos Kalnis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 8 | |
| 4 | 2 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | 9 |
| 8 | Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation | 22 |
| 9 | 31 | |
| 10 | 1 | |
| 11 | 37 | |
| 12 | 15 | |
| 13 | 3 | |
| 14 | 81 | |
| 15 | 12 | |
| 16 | 94 | |
| 17 | 17 | |
| 18 | 113 | |
| 19 | 17 | |
| 20 | Hierarchical constraint satisfaction in spatial databases | 10 |
About Panos Kalnis
Panos Kalnis is a scholar working on Signal Processing, Computer Networks and Communications and Artificial Intelligence, having authored 119 papers that have together received 5.7k indexed citations. Recurring topics across this work include Data Management and Algorithms (47 papers), Advanced Database Systems and Queries (25 papers) and Graph Theory and Algorithms (19 papers). The work is most often cited by research in Signal Processing (1.4k citations), Artificial Intelligence (3.2k citations) and Computer Science Applications (490 citations). Panos Kalnis has collaborated with scholars based in Saudi Arabia, China and Hong Kong. Frequent co-authors include Gabriel Ghinita, Shuo Shang, Dimitris Papadias, Nikos Mamoulis, Christian S. Jensen, Spiros Skiadopoulos, Kian‐Lee Tan, Lisi Chen, Yufei Tao and Kyriakos Mouratidis. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.
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.