Nathan Self
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
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- Stock Market Forecasting Methods
Papers in
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- Anomaly Detection Techniques and Applications 5
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- Complex Network Analysis Techniques 5
- Opinion Dynamics and Social Influence 4
- Co-authors
- Naren Ramakrishnan (22 shared papers)Chang‐Tien Lu (10 shared papers)P. J. Butler (3 shared papers)Parang Saraf (7 shared papers)Fang Jin (2 shared papers)Wei Wang (1 shared paper)Zhiqian Chen (6 shared papers)Kaiqun Fu (6 shared papers)
- Journals
- PLoS ONE (2 papers)Proceedings of the ACM on Human-Computer Interaction (1 paper)Big Data (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)ACM Transactions on Intelligent Systems and Technology (1 paper)
- Partner nations
- United StatesUnited KingdomEcuador
In The Last Decade
Nathan Self
22 papers receiving 169 citations
Peers
Comparison fields: 5 of 58
- Statistical and Nonlinear Physics 37
- Management Science and Operations Research 36
- Artificial Intelligence 63
- Signal Processing 21
- Transportation 11
Countries citing papers authored by Nathan Self
This map shows the geographic impact of Nathan Self'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 Nathan Self with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathan Self more than expected).
Fields of papers citing papers by Nathan Self
This network shows the impact of papers produced by Nathan Self. 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 Nathan Self. The network helps show where Nathan Self may publish in the future.
Co-authors
The 25 scholars most cited alongside Nathan Self, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 33 | |
| 2 | 2014 | 19 | |
| 3 | 2016 | 17 | |
| 4 | 2014 | 13 | |
| 5 | 2018 | 13 | |
| 6 | 2020 | 13 | |
| 7 | 2020 | 11 | |
| 8 | 2018 | 8 | |
| 9 | 2019 | 8 | |
| 10 | 2019 | 7 | |
| 11 | 2020 | 5 | |
| 12 | 2019 | 5 | |
| 13 | Bringing interactive visual analytics to the classroom for developing EDA skills | 2018 | 4 |
| 14 | 2023 | 4 | |
| 15 | 2019 | 3 | |
| 16 | 2019 | 3 | |
| 17 | 2018 | 3 | |
| 18 | 2020 | 2 | |
| 19 | 2021 | 2 | |
| 20 | 2024 | 2 |
About Nathan Self
Nathan Self is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Signal Processing and Epidemiology, having authored 24 papers that have together received 177 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (5 papers), Anomaly Detection Techniques and Applications (5 papers), Opinion Dynamics and Social Influence (4 papers), Data Visualization and Analytics (4 papers), Data Management and Algorithms (3 papers), Machine Learning in Materials Science (3 papers), Data-Driven Disease Surveillance (3 papers) and Wood and Agarwood Research (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (37 citations), Management Science and Operations Research (36 citations), Artificial Intelligence (63 citations), Signal Processing (21 citations) and Transportation (11 citations). Nathan Self has collaborated with scholars based in United States, United Kingdom and Ecuador. Frequent co-authors include Naren Ramakrishnan, Chang‐Tien Lu, P. J. Butler, Parang Saraf, Fang Jin, Wei Wang, Zhiqian Chen, Kaiqun Fu, Sathappan Muthiah and Rupinder Paul Khandpur. Their work appears in journals such as PLoS ONE, Proceedings of the ACM on Human-Computer Interaction, Big Data, ACM Transactions on Knowledge Discovery from Data and ACM Transactions on Intelligent Systems and Technology.
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.