Richard E. Neapolitan
- Artificial Intelligence top 2%
- Molecular Biology
- Management Science and Operations Research top 5%
- Computer Networks and Communications top 10%
- Information Systems top 5%
- Co-authors
- Peter M. JonesXia JiangShyam VisweswaranM. Michael BarmadaZexian ZengScott B. MorrisAdam BrufskyAlan Wells
- Topics
- Bayesian Modeling and Causal Inference (18 papers)Bioinformatics and Genomic Networks (11 papers)AI-based Problem Solving and Planning (9 papers)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Richard E. Neapolitan
53 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 159
- Artificial Intelligence 766
- Molecular Biology 272
- Management Science and Operations Research 175
- Computer Networks and Communications 141
- Information Systems 134
Countries citing papers authored by Richard E. Neapolitan
This map shows the geographic impact of Richard E. Neapolitan'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 Richard E. Neapolitan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard E. Neapolitan more than expected).
Fields of papers citing papers by Richard E. Neapolitan
This network shows the impact of papers produced by Richard E. Neapolitan. 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 Richard E. Neapolitan. The network helps show where Richard E. Neapolitan may publish in the future.
Co-authorship network of co-authors of Richard E. Neapolitan
This figure shows the co-authorship network connecting the top 25 collaborators of Richard E. Neapolitan. A scholar is included among the top collaborators of Richard E. Neapolitan 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 Richard E. Neapolitan. Richard E. Neapolitan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | 56 | |
| 3 | 8 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 15 | |
| 7 | 6 | |
| 8 | 28 | |
| 9 | 6 | |
| 10 | 9 | |
| 11 | 13 | |
| 12 | 74 | |
| 13 | Foundations of Algorithms, Fourth Edition | 3 |
| 14 | 6 | |
| 15 | Foundations of algorithms using C++ pseudocode (2nd ed.) | 8 |
| 16 | A survey of uncertain and approximate inference | 9 |
| 17 | A model theoretic approach to propositional fuzzy logic using Beth Tableaux | 7 |
| 18 | 1 | |
| 19 | Using Set Covering and Uncertain Reasoning to Determine Treatments | 3 |
| 20 | 9 |
About Richard E. Neapolitan
Richard E. Neapolitan is a scholar working on Artificial Intelligence, Management Science and Operations Research and General Decision Sciences, having authored 56 papers that have together received 1.5k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (18 papers), Bioinformatics and Genomic Networks (11 papers) and AI-based Problem Solving and Planning (9 papers). The work is most often cited by research in Artificial Intelligence (766 citations), Management Science and Operations Research (175 citations) and Software (37 citations). Richard E. Neapolitan has collaborated with scholars based in United States and South Korea. Frequent co-authors include Peter M. Jones, Xia Jiang, Shyam Visweswaran, M. Michael Barmada, Xia Jiang, Zexian Zeng, Scott B. Morris, Adam Brufsky, Alan Wells and Curt M. Horvath. Their work appears in journals such as PLoS ONE, Technometrics and BMC Bioinformatics.
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.