Scott Alfeld

485 total citations
11 papers, 229 citations indexed

About

Scott Alfeld is a scholar working on Artificial Intelligence, Computer Networks and Communications and Sociology and Political Science. According to data from OpenAlex, Scott Alfeld has authored 11 papers receiving a total of 229 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 2 papers in Computer Networks and Communications and 2 papers in Sociology and Political Science. Recurrent topics in Scott Alfeld's work include Adversarial Robustness in Machine Learning (5 papers), Spam and Phishing Detection (2 papers) and Complex Network Analysis Techniques (2 papers). Scott Alfeld is often cited by papers focused on Adversarial Robustness in Machine Learning (5 papers), Spam and Phishing Detection (2 papers) and Complex Network Analysis Techniques (2 papers). Scott Alfeld collaborates with scholars based in United States, Australia and Mexico. Scott Alfeld's 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 and has published in prestigious journals such as ACM Transactions on Knowledge Discovery from Data, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Scott Alfeld

11 papers receiving 219 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Scott Alfeld United States	4	166	46	45	45	34	11	229
Simon Razniewski Germany	10	237 1.4×	22 0.5×	46 1.0×	62 1.4×	12 0.4×	54	298
Gauri Jain India	7	220 1.3×	36 0.8×	112 2.5×	213 4.7×	43 1.3×	10	346
Emily Stark United States	9	157 0.9×	57 1.2×	82 1.8×	153 3.4×	49 1.4×	14	278
Mohammad Al-Rubaie United States	4	226 1.4×	35 0.8×	42 0.9×	51 1.1×	33 1.0×	5	307
Virat Shejwalkar United States	6	458 2.8×	16 0.3×	53 1.2×	40 0.9×	27 0.8×	11	502
Yu Suzuki Japan	8	100 0.6×	91 2.0×	9 0.2×	47 1.0×	22 0.6×	32	214
John Mark Agosta United States	6	134 0.8×	40 0.9×	89 2.0×	67 1.5×	55 1.6×	12	215
Xiaojie Guo China	8	307 1.8×	22 0.5×	39 0.9×	106 2.4×	23 0.7×	14	373
Georgios Pitsilis United Kingdom	7	227 1.4×	60 1.3×	115 2.6×	152 3.4×	34 1.0×	11	329
Nuria Rodríguez-Barroso Spain	6	307 1.8×	17 0.4×	39 0.9×	43 1.0×	46 1.4×	10	359

Countries citing papers authored by Scott Alfeld

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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11 of 11 papers shown

1.

Alfeld, Scott, et al.. (2023). Training-Time Attacks against K-nearest Neighbors. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 10053–10060. 1 indexed citations

2.

Miller, Benjamin A., et al.. (2023). Attacking Shortest Paths by Cutting Edges. ACM Transactions on Knowledge Discovery from Data. 18(2). 1–42. 1 indexed citations

3.

Rubinstein, Benjamin I. P., et al.. (2022). Hard to Forget: Poisoning Attacks on Certified Machine Unlearning. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 7691–7700. 37 indexed citations

4.

Liu, David, et al.. (2021). RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity. SSRN Electronic Journal. 1 indexed citations

5.

Alfeld, Scott, et al.. (2019). Attacking Data Transforming Learners at Training Time. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 3167–3174. 2 indexed citations

6.

Vorobeychik, Yevgeniy, et al.. (2018). Adversarial Classification on Social Networks. arXiv (Cornell University). 211–219. 3 indexed citations

7.

Alfeld, Scott, Xiaojin Zhu, & Paul Barford. (2017). Explicit Defense Actions Against Test-Set Attacks. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 12 indexed citations

8.

Alfeld, Scott, Xiaojin Zhu, & Paul Barford. (2016). Data Poisoning Attacks against Autoregressive Models. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 116 indexed citations

9.

Alfeld, Scott, et al.. (2016). An Empirical Study of Web Cookies. 891–901. 53 indexed citations

10.

Alfeld, Scott & Paul Barford. (2014). Targeted residual analysis for improving electric load forecasting. 12. 459–466. 1 indexed citations

11.

Alfeld, Scott, Carol Barford, & Paul Barford. (2012). Toward an analytic framework for the electrical power grid. 1–4. 2 indexed citations

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.

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