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
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- Complex Network Analysis Techniques 4
- Opinion Dynamics and Social Influence 3
- Co-authors
- David F. Gleich (4 shared papers)Kun He (1 shared paper)David Bindel (1 shared paper)Yixuan Li (1 shared paper)John E. Hopcroft (1 shared paper)Biaobin Jiang (1 shared paper)Michael Gribskov (1 shared paper)Daniel Himmelstein (2 shared papers)
- Journals
- GigaScience (2 papers)Internet Mathematics (1 paper)ACM Transactions on Knowledge Discovery from Data (1 paper)Bioinformatics (1 paper)Purdue e-Pubs (Purdue University System) (1 paper)
- Partner nations
- United StatesChina
In The Last Decade
Kyle Kloster
8 papers receiving 244 citations
Peers
Comparison fields: 5 of 41
- Statistical and Nonlinear Physics 168
- Artificial Intelligence 136
- Transportation 22
- Computer Networks and Communications 47
- Computational Theory and Mathematics 30
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 15 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 | 121 | |
| 2 | 2018 | 64 | |
| 3 | 2017 | 42 | |
| 4 | 2024 | 8 | |
| 5 | 2022 | 6 | |
| 6 | 2014 | 5 | |
| 7 | A Fast Relaxation Method for Computing a Column of the Matrix Exponential of Stochastic Matrices from Large, Sparse Networks. | 2013 | 2 |
| 8 | 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, Computer Networks and Communications and Geometry and Topology, having authored 8 papers that have together received 249 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (4 papers), Bioinformatics and Genomic Networks (3 papers), Opinion Dynamics and Social Influence (3 papers), Advanced Graph Neural Networks (2 papers), Neural Networks and Applications (2 papers), Peer-to-Peer Network Technologies (1 paper), Gene expression and cancer classification (1 paper) and Biomedical Text Mining and Ontologies (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (168 citations), Artificial Intelligence (136 citations), Transportation (22 citations), Computer Networks and Communications (47 citations) and Computational Theory and Mathematics (30 citations). Kyle Kloster has collaborated with scholars based in United States and China. Frequent co-authors include David F. Gleich, Kun He, David Bindel, Yixuan Li, John E. Hopcroft, Biaobin Jiang, Michael Gribskov, Daniel Himmelstein, Michael W. Nagle and Casey S. Greene. Their work appears in journals such as GigaScience, Internet Mathematics, ACM Transactions on Knowledge Discovery from Data, Bioinformatics and Purdue e-Pubs (Purdue University System).
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.