Markus Hagenbuchner
- Artificial Intelligence top 0.2%
- Computer Vision and Pattern Recognition top 0.5%
- Information Systems top 0.5%
- Statistical and Nonlinear Physics top 1%
- Computer Networks and Communications top 2%
- Co-authors
- Ah Chung TsoiFranco ScarselliM. GoriGabriele MonfardiniAlessandro SperdutiJie YangNagesh ShuklaKhin Than Win
- Topics
- Neural Networks and Applications (22 papers)Web Data Mining and Analysis (14 papers)Face and Expression Recognition (8 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionStatistical and Nonlinear Physics
In The Last Decade
Markus Hagenbuchner
58 papers receiving 5.9k citations
Hit Papers
Peers
Comparison fields: 5 of 183
- Artificial Intelligence 3.1k
- Computer Vision and Pattern Recognition 1.5k
- Information Systems 820
- Statistical and Nonlinear Physics 640
- Computer Networks and Communications 637
Countries citing papers authored by Markus Hagenbuchner
This map shows the geographic impact of Markus Hagenbuchner'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 Markus Hagenbuchner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Hagenbuchner more than expected).
Fields of papers citing papers by Markus Hagenbuchner
This network shows the impact of papers produced by Markus Hagenbuchner. 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 Markus Hagenbuchner. The network helps show where Markus Hagenbuchner may publish in the future.
Co-authorship network of co-authors of Markus Hagenbuchner
This figure shows the co-authorship network connecting the top 25 collaborators of Markus Hagenbuchner. A scholar is included among the top collaborators of Markus Hagenbuchner 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 Markus Hagenbuchner. Markus Hagenbuchner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 14 | |
| 4 | 30 | |
| 5 | Deep learning for energy expenditure prediction in pre-school children | 6 |
| 6 | 39 | |
| 7 | 37 | |
| 8 | 9 | |
| 9 | 4 | |
| 10 | Cost-Sensitive Cascade Graph Neural Networks | 1 |
| 11 | Sparsity issues in self-organizing-maps for structures | 3 |
| 12 | Projection of undirected and non-positional graphs using self organizing maps | 5 |
| 13 | 21 | |
| 14 | Self-Organizing Maps for cyclic and unbounded graphs. | 2 |
| 15 | 133 | |
| 16 | "Kernelized" Self-Organizing Maps for Structured Data | 2 |
| 17 | Investigation into the application of graph neural networks to large-scale recommender systems | 4 |
| 18 | Contextual Processing of Graphs using Self-Organizing Maps. | 10 |
| 19 | A Simple Focused Crawler. | 9 |
| 20 | A benchmark for testing adaptive systems on structured data. | 2 |
About Markus Hagenbuchner
Markus Hagenbuchner is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 62 papers that have together received 6.1k indexed citations. Recurring topics across this work include Neural Networks and Applications (22 papers), Web Data Mining and Analysis (14 papers) and Face and Expression Recognition (8 papers). The work is most often cited by research in Artificial Intelligence (3.1k citations), Computer Vision and Pattern Recognition (1.5k citations) and Statistical and Nonlinear Physics (640 citations). Markus Hagenbuchner has collaborated with scholars based in Australia, Italy and Hong Kong. Frequent co-authors include Ah Chung Tsoi, Franco Scarselli, M. Gori, Gabriele Monfardini, Alessandro Sperduti, Jie Yang, Nagesh Shukla, Khin Than Win, Marco Maggini and Zhiyong Wang. Their work appears in journals such as Medicine & Science in Sports & Exercise, Medical Physics and IEEE Transactions on Neural Networks and Learning Systems.
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.