Mark Heimann
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
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- Advanced Graph Neural Networks
- Domain Adaptation and Few-Shot Learning
Papers in ⓘ
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- Complex Network Analysis Techniques 4
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- Advanced Graph Neural Networks 4
- Co-authors
- Danai Koutra (5 shared papers)Di Jin (2 shared papers)Tara Safavi (2 shared papers)Junchen Jin (1 shared paper)Wei‐Chen Lee (1 shared paper)Yujun Yan (1 shared paper)Jayaraman J. Thiagarajan (1 shared paper)Jiong Zhu (1 shared paper)
- Journals
- ACM Transactions on Knowledge Discovery from Data (1 paper)IEEE Transactions on Visualization and Computer Graphics (1 paper)Journal of Chemical Theory and Computation (1 paper)arXiv (Cornell University) (1 paper)IMAPSource Proceedings (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Mark Heimann
9 papers receiving 56 citations
Peers
Comparison fields: 5 of 31
- Statistical and Nonlinear Physics 28
- Artificial Intelligence 40
- Computer Vision and Pattern Recognition 11
- Information Systems and Management 3
- Communication 3
Countries citing papers authored by Mark Heimann
This map shows the geographic impact of Mark Heimann'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 Mark Heimann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Heimann more than expected).
Fields of papers citing papers by Mark Heimann
This network shows the impact of papers produced by Mark Heimann. 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 Mark Heimann. The network helps show where Mark Heimann may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Heimann, 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 | 2021 | 16 | |
| 2 | 2019 | 11 | |
| 3 | 2019 | 7 | |
| 4 | 2023 | 7 | |
| 5 | 2019 | 4 | |
| 6 | 2022 | 4 | |
| 7 | Generalizing Graph Neural Networks Beyond Homophily. | 2020 | 3 |
| 8 | 2023 | 3 | |
| 9 | 2015 | 2 | |
| 10 | 2024 | 1 |
About Mark Heimann
Mark Heimann is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 10 papers that have together received 58 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (4 papers), Advanced Graph Neural Networks (4 papers), Semiconductor materials and interfaces (1 paper), Caching and Content Delivery (1 paper), Mental Health via Writing (1 paper), Multimodal Machine Learning Applications (1 paper), RNA and protein synthesis mechanisms (1 paper) and Bacterial Genetics and Biotechnology (1 paper). The work is most often cited by research in Statistical and Nonlinear Physics (28 citations), Artificial Intelligence (40 citations), Computer Vision and Pattern Recognition (11 citations), Information Systems and Management (3 citations) and Communication (3 citations). Mark Heimann has collaborated with scholars based in United States and Germany. Frequent co-authors include Danai Koutra, Di Jin, Tara Safavi, Junchen Jin, Wei‐Chen Lee, Yujun Yan, Jayaraman J. Thiagarajan, Jiong Zhu, Lingxiao Zhao and Leman Akoglu. Their work appears in journals such as ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Visualization and Computer Graphics, Journal of Chemical Theory and Computation, arXiv (Cornell University) and IMAPSource Proceedings.
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.