Abir De
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Topic Modeling
- Sentiment Analysis and Opinion Mining
Papers in
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- Complex Network Analysis Techniques 17
- Opinion Dynamics and Social Influence 13
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- Advanced Graph Neural Networks 9
- Topic Modeling 4
- Machine Learning and Algorithms 3
- Co-authors
- Niloy Ganguly (15 shared papers)Manuel Gomez-Rodriguez (9 shared papers)Sourangshu Bhattacharya (10 shared papers)Soumen Chakrabarti (10 shared papers)Utkarsh Upadhyay (4 shared papers)Ali Zarezade (3 shared papers)Behzad Tabibian (1 shared paper)Bernhard Schölkopf (1 shared paper)
- Journals
- ACM Transactions on Intelligent Systems and Technology (2 papers)ACM Transactions on the Web (1 paper)PLoS Computational Biology (1 paper)Journal of Machine Learning Research (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- IndiaGermanyUnited States
In The Last Decade
Abir De
33 papers receiving 418 citations
Peers
Comparison fields: 5 of 86
- Statistical and Nonlinear Physics 170
- Artificial Intelligence 175
- Transportation 35
- Computer Science Applications 21
- Computational Mathematics 2
Countries citing papers authored by Abir De
This map shows the geographic impact of Abir De'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 Abir De with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abir De more than expected).
Fields of papers citing papers by Abir De
This network shows the impact of papers produced by Abir De. 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 Abir De. The network helps show where Abir De may publish in the future.
Co-authors
The 25 scholars most cited alongside Abir De, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 89 | |
| 2 | 2014 | 36 | |
| 3 | 2016 | 36 | |
| 4 | Learning and Forecasting Opinion Dynamics in Social Networks | 2016 | 35 |
| 5 | Deep Reinforcement Learning of Marked Temporal Point Processes | 2018 | 35 |
| 6 | 2015 | 24 | |
| 7 | 2017 | 17 | |
| 8 | 2020 | 16 | |
| 9 | 2019 | 15 | |
| 10 | 2013 | 13 | |
| 11 | 2022 | 12 | |
| 12 | Steering Social Activity: A Stochastic Optimal Control Point Of View | 2018 | 11 |
| 13 | 2017 | 10 | |
| 14 | 2021 | 10 | |
| 15 | 2022 | 9 | |
| 16 | 2018 | 9 | |
| 17 | 2017 | 8 | |
| 18 | 2019 | 8 | |
| 19 | 2021 | 7 | |
| 20 | 2018 | 7 |
About Abir De
Abir De is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Transportation, Computer Vision and Pattern Recognition and Molecular Biology, having authored 38 papers that have together received 437 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (17 papers), Opinion Dynamics and Social Influence (13 papers), Advanced Graph Neural Networks (9 papers), Human Mobility and Location-Based Analysis (8 papers), Topic Modeling (4 papers), Machine Learning and Algorithms (3 papers), Quantum many-body systems (3 papers) and Point processes and geometric inequalities (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (170 citations), Artificial Intelligence (175 citations), Transportation (35 citations), Computer Science Applications (21 citations) and Computational Mathematics (2 citations). Abir De has collaborated with scholars based in India, Germany and United States. Frequent co-authors include Niloy Ganguly, Manuel Gomez-Rodriguez, Sourangshu Bhattacharya, Soumen Chakrabarti, Utkarsh Upadhyay, Ali Zarezade, Behzad Tabibian, Bernhard Schölkopf, Parantapa Bhattacharya and Isabel Valera. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, ACM Transactions on the Web, PLoS Computational Biology, Journal of Machine Learning Research and Proceedings of the National Academy of Sciences.
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.