Arijit Khan
- Signal Processing top 2%
- Data Management and Algorithms 21
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- Caching and Content Delivery 8
- Advanced Database Systems and Queries 8
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- Complex Network Analysis Techniques 20
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- Graph Theory and Algorithms 27
- Information Systems top 1%
- Blockchain Technology Applications and Security 7
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- Advanced Graph Neural Networks 36
- Explainable Artificial Intelligence (XAI) 7
- Co-authors
- Xifeng YanTao ShuCharų C. AggarwalNikos AnerousisGustavo AlonsoYinghui WuShengqi YangBo Zong
- Journals
- Proceedings of the VLDB Endowment (10 papers)IEEE Transactions on Knowledge and Data Engineering (4 papers)ACM SIGMOD Record (3 papers)
- Partner nations
- SingaporeDenmarkUnited States
In The Last Decade
Arijit Khan
68 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 78
- Signal Processing 333
- Computer Networks and Communications 603
- Statistical and Nonlinear Physics 303
- Computer Vision and Pattern Recognition 494
- Information Systems 506
Countries citing papers authored by Arijit Khan
This map shows the geographic impact of Arijit Khan'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 Arijit Khan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arijit Khan more than expected).
Fields of papers citing papers by Arijit Khan
This network shows the impact of papers produced by Arijit Khan. 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 Arijit Khan. The network helps show where Arijit Khan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Arijit Khan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 3 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 2 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 3 | |
| 8 | 2024 | 0 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 0 | |
| 11 | 2023 | 9 | |
| 12 | 2022 | 11 | |
| 13 | 2020 | 23 | |
| 14 | 2018 | 52 | |
| 15 | Probabilistic Entity Resolution with Imperfect Crowd | 2017 | 1 |
| 16 | 2016 | 8 | |
| 17 | Biomedical Data Management and Graph Online Querying : VLDB 2015 Workshops, Big-O (Q) and DMAH, Waikoloa, HI, USA, August 31 September 4, 2015, Revised Selected Papers | 2016 | 3 |
| 18 | 2014 | 6 | |
| 19 | 2011 | 83 | |
| 20 | 2008 | 45 |
About Arijit Khan
Arijit Khan is a scholar working on Signal Processing, Statistical and Nonlinear Physics and Artificial Intelligence, having authored 76 papers that have together received 1.5k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (36 papers), Graph Theory and Algorithms (27 papers), Data Management and Algorithms (21 papers), Complex Network Analysis Techniques (20 papers), Caching and Content Delivery (8 papers), Advanced Database Systems and Queries (8 papers), Explainable Artificial Intelligence (XAI) (7 papers) and Blockchain Technology Applications and Security (7 papers). The work is most often cited by research in Signal Processing (333 citations), Computer Networks and Communications (603 citations) and Statistical and Nonlinear Physics (303 citations). Arijit Khan has collaborated with scholars based in Singapore, Denmark and United States. Frequent co-authors include Xifeng Yan, Tao Shu, Charų C. Aggarwal, Nikos Anerousis, Gustavo Alonso, Yinghui Wu, Shengqi Yang, Bo Zong, Sourav Sen Gupta and Lawrence Jenkins. Their work appears in journals such as Proceedings of the VLDB Endowment, IEEE Transactions on Knowledge and Data Engineering, ACM SIGMOD Record, Scientific Reports and Knowledge-Based Systems.
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.