Geoff Hulten
About
In The Last Decade
Geoff Hulten
19 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 2.8k
- Signal Processing 1.0k
- Computer Networks and Communications 923
- Information Systems 777
- Management Science and Operations Research 223
Countries citing papers authored by Geoff Hulten
This map shows the geographic impact of Geoff Hulten'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 Geoff Hulten with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoff Hulten more than expected).
Fields of papers citing papers by Geoff Hulten
This network shows the impact of papers produced by Geoff Hulten. 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 Geoff Hulten. The network helps show where Geoff Hulten may publish in the future.
Co-authorship network of co-authors of Geoff Hulten
This figure shows the co-authorship network connecting the top 25 collaborators of Geoff Hulten. A scholar is included among the top collaborators of Geoff Hulten 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 Geoff Hulten. Geoff Hulten is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 23 | |
| 3 | 61 | |
| 4 | 2 | |
| 5 | 99 | |
| 6 | 244 | |
| 7 | Learning at Low False Positive Rates. | 27 |
| 8 | Trends in Spam Products and Methods. | 19 |
| 9 | 17 | |
| 10 | Learning Bayesian Networks From Dependency Networks: A Preliminary Study | 14 |
| 11 | 102 | |
| 12 | Research on Statistical Relational Learning at the University of Washington | 1 |
| 13 | Mining Massive Relational Databases | 10 |
| 14 | 1 | |
| 15 | 47 | |
| 16 | A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering | 109 |
| 17 | Mining time-changing data streams breakdown → | 1073 |
| 18 | Catching up with the Data: Research Issues in Mining Data Streams. | 76 |
| 19 | Mining high-speed data streams breakdown → | 1298 |
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.