Kyle Kloster
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Artificial Intelligence top 10%
- Advanced Graph Neural Networks
- Advanced Clustering Algorithms Research
Papers in
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- Advanced Graph Neural Networks 2
- Neural Networks and Applications 2
- Algorithms and Data Compression 2
- Machine Learning and Data Classification 1
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- Complex Network Analysis Techniques 6
- Opinion Dynamics and Social Influence 4
- Co-authors
- David F. Gleich (6 shared papers)Yixuan Li (1 shared paper)John E. Hopcroft (1 shared paper)David Bindel (1 shared paper)Kun He (1 shared paper)Michael Gribskov (1 shared paper)Biaobin Jiang (1 shared paper)Michael W. Nagle (2 shared papers)
- Journals
- GigaScience (2 papers)Bioinformatics (1 paper)Internet Mathematics (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Lecture notes in computer science (2 papers)
- Partner nations
- United StatesChina
In The Last Decade
Kyle Kloster
12 papers receiving 302 citations
Peers
Comparison fields: 5 of 49
- Statistical and Nonlinear Physics 196
- Artificial Intelligence 163
- Transportation 24
- Computational Mathematics 2
- Computer Networks and Communications 57
Countries citing papers authored by Kyle Kloster
This map shows the geographic impact of Kyle Kloster'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 Kyle Kloster with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyle Kloster more than expected).
Fields of papers citing papers by Kyle Kloster
This network shows the impact of papers produced by Kyle Kloster. 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 Kyle Kloster. The network helps show where Kyle Kloster may publish in the future.
Co-authors
The 17 scholars most cited alongside Kyle Kloster, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 136 | |
| 2 | 2018 | 69 | |
| 3 | 2017 | 45 | |
| 4 | 2024 | 12 | |
| 5 | 2015 | 10 | |
| 6 | 2022 | 7 | |
| 7 | 2018 | 7 | |
| 8 | 2014 | 7 | |
| 9 | 2013 | 6 | |
| 10 | 2019 | 5 | |
| 11 | A Fast Relaxation Method for Computing a Column of the Matrix Exponential of Stochastic Matrices from Large, Sparse Networks. | 2013 | 2 |
| 12 | Graph diffusions and matrix functions: Fast algorithms and localization results | 2016 | 1 |
About Kyle Kloster
Kyle Kloster is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Molecular Biology, Geometry and Topology and Computer Networks and Communications, having authored 12 papers that have together received 307 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (6 papers), Opinion Dynamics and Social Influence (4 papers), Graph theory and applications (3 papers), Bioinformatics and Genomic Networks (3 papers), Advanced Graph Neural Networks (2 papers), Neural Networks and Applications (2 papers), Algorithms and Data Compression (2 papers) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (196 citations), Artificial Intelligence (163 citations), Transportation (24 citations), Computational Mathematics (2 citations) and Computer Networks and Communications (57 citations). Kyle Kloster has collaborated with scholars based in United States and China. Frequent co-authors include David F. Gleich, Yixuan Li, John E. Hopcroft, David Bindel, Kun He, Michael Gribskov, Biaobin Jiang, Michael W. Nagle, Daniel Himmelstein and Casey S. Greene. Their work appears in journals such as GigaScience, Bioinformatics, Internet Mathematics, ACM Transactions on Knowledge Discovery from Data and Lecture notes in computer science.
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.