Scott Alfeld
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Computer Networks and Communications
- Information Systems top 10%
- Sociology and Political Science
- Co-authors
- Paul BarfordXiaojin ZhuS. MuthukrishnanBenjamin I. P. RubinsteinYevgeniy VorobeychikCarol BarfordTina Eliassi‐RadDavid Liu
- Topics
- Adversarial Robustness in Machine Learning (5 papers)Spam and Phishing Detection (2 papers)Complex Network Analysis Techniques (2 papers)
- Journals
- ACM Transactions on Knowledge Discovery from DataarXiv (Cornell University)Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesAustraliaMexico
In The Last Decade
Scott Alfeld
11 papers receiving 219 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 166
- Signal Processing 46
- Computer Networks and Communications 45
- Information Systems 45
- Sociology and Political Science 34
Countries citing papers authored by Scott Alfeld
This map shows the geographic impact of Scott Alfeld'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 Scott Alfeld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Alfeld more than expected).
Fields of papers citing papers by Scott Alfeld
This network shows the impact of papers produced by Scott Alfeld. 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 Scott Alfeld. The network helps show where Scott Alfeld may publish in the future.
Co-authorship network of co-authors of Scott Alfeld
This figure shows the co-authorship network connecting the top 25 collaborators of Scott Alfeld. A scholar is included among the top collaborators of Scott Alfeld 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 Scott Alfeld. Scott Alfeld is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 37 | |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 12 | |
| 8 | 116 | |
| 9 | 53 | |
| 10 | 1 | |
| 11 | 2 |
About Scott Alfeld
Scott Alfeld is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Statistics and Probability, having authored 11 papers that have together received 229 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (5 papers), Spam and Phishing Detection (2 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (166 citations), Signal Processing (46 citations) and Computer Science Applications (22 citations). Scott Alfeld has collaborated with scholars based in United States, Australia and Mexico. Frequent co-authors include Paul Barford, Xiaojin Zhu, S. Muthukrishnan, Benjamin I. P. Rubinstein, Yevgeniy Vorobeychik, Carol Barford, Tina Eliassi‐Rad, David Liu, Will Fleisher and Benjamin A. Miller. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.