Grant Schoenebeck
- Artificial Intelligence top 2%
- Information Systems top 5%
- Computer Networks and Communications top 5%
- Computational Theory and Mathematics top 5%
- Management Science and Operations Research top 5%
- Co-authors
- Sarita Yardidanah boydDaniel M. RomeroAaron RothYuqing KongMichael E. HouleBo LiJames Bailey
- Topics
- Complex Network Analysis Techniques (11 papers)Mobile Crowdsensing and Crowdsourcing (11 papers)Auction Theory and Applications (10 papers)
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Grant Schoenebeck
41 papers receiving 864 citations
Peers
Comparison fields: 5 of 77
- Artificial Intelligence 517
- Information Systems 231
- Computer Networks and Communications 206
- Computational Theory and Mathematics 166
- Management Science and Operations Research 143
Countries citing papers authored by Grant Schoenebeck
This map shows the geographic impact of Grant Schoenebeck'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 Grant Schoenebeck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Grant Schoenebeck more than expected).
Fields of papers citing papers by Grant Schoenebeck
This network shows the impact of papers produced by Grant Schoenebeck. 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 Grant Schoenebeck. The network helps show where Grant Schoenebeck may publish in the future.
Co-authorship network of co-authors of Grant Schoenebeck
This figure shows the co-authorship network connecting the top 25 collaborators of Grant Schoenebeck. A scholar is included among the top collaborators of Grant Schoenebeck 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 Grant Schoenebeck. Grant Schoenebeck is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 2 | |
| 3 | 3 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | 7 | |
| 7 | 6 | |
| 8 | Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | 176 |
| 9 | 10 | |
| 10 | 12 | |
| 11 | 17 | |
| 12 | 2 | |
| 13 | 3 | |
| 14 | How Complex Contagions Spread and Spread Quickly. | 1 |
| 15 | 5 | |
| 16 | 13 | |
| 17 | Reaching Consensus on Social Networks | 20 |
| 18 | 220 | |
| 19 | 3 | |
| 20 | The Computational Complexity of Nash Equilibria in Concisely Represented Games | 25 |
About Grant Schoenebeck
Grant Schoenebeck is a scholar working on Computer Science Applications, Management Science and Operations Research and Statistical and Nonlinear Physics, having authored 43 papers that have together received 918 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (11 papers), Mobile Crowdsensing and Crowdsourcing (11 papers) and Auction Theory and Applications (10 papers). The work is most often cited by research in Computer Science Applications (101 citations), Computational Mathematics (10 citations) and Artificial Intelligence (517 citations). Grant Schoenebeck has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Sarita Yardi, danah boyd, Daniel M. Romero, Aaron Roth, Yuqing Kong, Michael E. Houle, Bo Li, James Bailey, Yisen Wang and Dawn Song. Their work appears in journals such as Proceedings of the VLDB Endowment, Journal of Artificial Intelligence Research and First Monday.
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.