Luh Yen
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Artificial Intelligence top 5%
- Advanced Graph Neural Networks
- Advanced Clustering Algorithms Research
Papers in
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- Complex Network Analysis Techniques 9
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- Advanced Graph Neural Networks 3
- Reinforcement Learning in Robotics 3
- Advanced Clustering Algorithms Research 2
- Co-authors
- Marco Saerens (12 shared papers)François Fouss (10 shared papers)Alain Pirotte (6 shared papers)Amin Mantrach (2 shared papers)Masashi Shimbo (2 shared papers)Christine Decaestecker (1 shared paper)Michel Verleysen (1 shared paper)Jean-Michel Renders (1 shared paper)
In The Last Decade
Luh Yen
12 papers receiving 454 citations
Peers
Comparison fields: 5 of 77
- Statistical and Nonlinear Physics 220
- Artificial Intelligence 203
- Computational Theory and Mathematics 74
- Computer Vision and Pattern Recognition 80
- Geometry and Topology 31
Countries citing papers authored by Luh Yen
This map shows the geographic impact of Luh Yen'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 Luh Yen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luh Yen more than expected).
Fields of papers citing papers by Luh Yen
This network shows the impact of papers produced by Luh Yen. 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 Luh Yen. The network helps show where Luh Yen may publish in the future.
Co-authors
The 8 scholars most cited alongside Luh Yen, 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 | 2012 | 83 | |
| 2 | 2009 | 71 | |
| 3 | 2006 | 63 | |
| 4 | 2008 | 55 | |
| 5 | Clustering using a random walk based distance measure | 2005 | 52 |
| 6 | 2008 | 48 | |
| 7 | 2009 | 35 | |
| 8 | 2008 | 19 | |
| 9 | A novel way of computing similarities between nodes of a graph, with application to collaborative filtering and subspace projection of the graph nodes | 2006 | 17 |
| 10 | 2010 | 16 | |
| 11 | Optimal Tuning of Continual Online Exploration in Reinforcement Learning | 2006 | 7 |
| 12 | Tuning Continual Exploration in Reinforcement Learning | 2007 | 4 |
About Luh Yen
Luh Yen is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Geometry and Topology, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 12 papers that have together received 470 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (9 papers), Advanced Graph Neural Networks (3 papers), Reinforcement Learning in Robotics (3 papers), Graph theory and applications (3 papers), Data Management and Algorithms (2 papers), Face and Expression Recognition (2 papers), Advanced Bandit Algorithms Research (2 papers) and Advanced Clustering Algorithms Research (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (220 citations), Artificial Intelligence (203 citations), Computational Theory and Mathematics (74 citations), Computer Vision and Pattern Recognition (80 citations) and Geometry and Topology (31 citations). Luh Yen has collaborated with scholars based in Belgium and Japan. Frequent co-authors include Marco Saerens, François Fouss, Alain Pirotte, Amin Mantrach, Masashi Shimbo, Christine Decaestecker, Michel Verleysen and Jean-Michel Renders. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Data & Knowledge Engineering, Neural Computation, Neural Networks and IEEE Transactions on Knowledge and Data Engineering.
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.