Mark Heimann

649 total citations
10 papers, 58 citations indexed

About

Mark Heimann is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Networks and Communications. According to data from OpenAlex, Mark Heimann has authored 10 papers receiving a total of 58 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 2 papers in Computer Networks and Communications. Recurrent topics in Mark Heimann's work include Complex Network Analysis Techniques (4 papers), Advanced Graph Neural Networks (4 papers) and Protein Structure and Dynamics (1 paper). Mark Heimann is often cited by papers focused on Complex Network Analysis Techniques (4 papers), Advanced Graph Neural Networks (4 papers) and Protein Structure and Dynamics (1 paper). Mark Heimann collaborates with scholars based in United States and Germany. Mark Heimann's co-authors include Danai Koutra, Di Jin, Tara Safavi, Junchen Jin, Wei‐Chen Lee, Valerio Pascucci, Rushil Anirudh, Lingxiao Zhao, Attila Gyulassy and Jayaraman J. Thiagarajan and has published in prestigious journals such as Journal of Chemical Theory and Computation, IEEE Transactions on Visualization and Computer Graphics and ACM Transactions on Knowledge Discovery from Data.

In The Last Decade

Mark Heimann

9 papers receiving 56 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark Heimann United States	5	40	28	11	11	7	10	58
Anton Tsitsulin United States	3	36 0.9×	22 0.8×	7 0.6×	11 1.0×	8 1.1×	8	44
Amir Hosein Khasahmadi United States	3	32 0.8×	6 0.2×	6 0.5×	20 1.8×	8 1.1×	5	52
Kenta Oono Japan	3	30 0.8×	10 0.4×	10 0.9×	13 1.2×	7 1.0×	5	48
Xutan Peng United Kingdom	7	91 2.3×	8 0.3×	7 0.6×	10 0.9×	6 0.9×	13	95
Laure Soulier France	7	62 1.6×	9 0.3×	6 0.5×	21 1.9×	25 3.6×	25	93
Jianlong Tan China	4	30 0.8×	10 0.4×	3 0.3×	4 0.4×	8 1.1×	10	52
Bruce W. Herr United States	6	15 0.4×	8 0.3×	15 1.4×	16 1.5×	6 0.9×	10	51
Aditeya Pandey United States	6	18 0.5×	6 0.2×	8 0.7×	46 4.2×	6 0.9×	13	79
Edward De Brouwer Belgium	5	34 0.8×	15 0.5×	2 0.2×	7 0.6×	4 0.6×	10	51
Kiril Gashteovski Germany	6	162 4.0×	9 0.3×	11 1.0×	8 0.7×	19 2.7×	17	168

Countries citing papers authored by Mark Heimann

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Mark Heimann

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Heimann. A scholar is included among the top collaborators of Mark Heimann 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 Mark Heimann. Mark Heimann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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10 of 10 papers shown

1.

Aydin, Fikret, Mark Heimann, Loïc Pottier, et al.. (2024). Generating Protein Structures for Pathway Discovery Using Deep Learning. Journal of Chemical Theory and Computation. 20(20). 8795–8806. 1 indexed citations

2.

Gyulassy, Attila, et al.. (2023). Exploring Classification of Topological Priors With Machine Learning for Feature Extraction. IEEE Transactions on Visualization and Computer Graphics. 30(7). 3959–3972. 3 indexed citations

3.

Heimann, Mark, et al.. (2023). Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2478–2486. 7 indexed citations

4.

Koutra, Danai, et al.. (2022). CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 4747–4751. 4 indexed citations

5.

Jin, Junchen, Mark Heimann, Di Jin, & Danai Koutra. (2021). Toward Understanding and Evaluating Structural Node Embeddings. ACM Transactions on Knowledge Discovery from Data. 16(3). 1–32. 16 indexed citations

6.

Zhu, Jiong, Yujun Yan, Lingxiao Zhao, et al.. (2020). Generalizing Graph Neural Networks Beyond Homophily.. arXiv (Cornell University). 3 indexed citations

7.

Jin, Di, et al.. (2019). Smart Roles. 2923–2933. 11 indexed citations

8.

Heimann, Mark, Tara Safavi, & Danai Koutra. (2019). Distribution of Node Embeddings as Multiresolution Features for Graphs. 289–298. 7 indexed citations

9.

Voges, S., Karl‐Friedrich Becker, Bernd Schröder, et al.. (2019). Highly Miniaturized Integrated Sensor Nodes for Industry 4.0. IMAPSource Proceedings. 2019(1). 415–422. 4 indexed citations

10.

Rudolph, Thomas, Mark Heimann, A. Schwabedissen, et al.. (2015). 3 Years of High Quality mc-Si Q.ANTUM Production Experience – Approaches for Efficient Cell and Module Development. EU PVSEC. 273–278. 2 indexed citations

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.

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