Bahman Bahmani
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 5%
- Information Systems top 2%
- Statistical and Nonlinear Physics top 2%
- Co-authors
- Ravi KumarSergei VassilvitskiiAshish GoelBenjamin MoseleyAndrea VattaniAbdur ChowdhuryKaushik ChakrabartiDong Xin
- Topics
- Caching and Content Delivery (4 papers)Complex Network Analysis Techniques (3 papers)Data Management and Algorithms (3 papers)
- Journals
- Proceedings of the VLDB EndowmentTheory of ComputingarXiv (Cornell University)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Bahman Bahmani
11 papers receiving 916 citations
Hit Papers
Peers
Comparison fields: 5 of 82
- Artificial Intelligence 523
- Computer Vision and Pattern Recognition 340
- Computer Networks and Communications 271
- Information Systems 263
- Statistical and Nonlinear Physics 253
Countries citing papers authored by Bahman Bahmani
This map shows the geographic impact of Bahman Bahmani'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 Bahman Bahmani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bahman Bahmani more than expected).
Fields of papers citing papers by Bahman Bahmani
This network shows the impact of papers produced by Bahman Bahmani. 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 Bahman Bahmani. The network helps show where Bahman Bahmani may publish in the future.
Co-authorship network of co-authors of Bahman Bahmani
This figure shows the co-authorship network connecting the top 25 collaborators of Bahman Bahmani. A scholar is included among the top collaborators of Bahman Bahmani 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 Bahman Bahmani. Bahman Bahmani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 2 | |
| 3 | 55 | |
| 4 | 134 | |
| 5 | 18 | |
| 6 | Scalable k-means++breakdown → | 405 |
| 7 | 41 | |
| 8 | 87 | |
| 9 | Fast Incremental and Personalized PageRank over Distributed Main Memory Databases | 2 |
| 10 | 1 | |
| 11 | 194 |
About Bahman Bahmani
Bahman Bahmani is a scholar working on Signal Processing, Discrete Mathematics and Combinatorics and Computer Networks and Communications, having authored 11 papers that have together received 956 indexed citations. Recurring topics across this work include Caching and Content Delivery (4 papers), Complex Network Analysis Techniques (3 papers) and Data Management and Algorithms (3 papers). The work is most often cited by research in Signal Processing (252 citations), Statistical and Nonlinear Physics (253 citations) and Computer Vision and Pattern Recognition (340 citations). Bahman Bahmani has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ravi Kumar, Sergei Vassilvitskii, Ashish Goel, Benjamin Moseley, Andrea Vattani, Abdur Chowdhury, Kaushik Chakrabarti, Dong Xin, Eli Upfal and Mohammad Mahdian. Their work appears in journals such as Proceedings of the VLDB Endowment, Theory of Computing 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.