Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An End-to-End Deep Learning Architecture for Graph Classification
2018819 citationsMuhan Zhang, Zhicheng Cui et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Marion Neumann
Since
Specialization
Citations
This map shows the geographic impact of Marion Neumann'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 Marion Neumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marion Neumann more than expected).
This network shows the impact of papers produced by Marion Neumann. 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 Marion Neumann. The network helps show where Marion Neumann may publish in the future.
Co-authorship network of co-authors of Marion Neumann
This figure shows the co-authorship network connecting the top 25 collaborators of Marion Neumann.
A scholar is included among the top collaborators of Marion Neumann 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 Marion Neumann. Marion Neumann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Muhan, Zhicheng Cui, Marion Neumann, & Yixin Chen. (2018). An End-to-End Deep Learning Architecture for Graph Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).819 indexed citations breakdown →
Kriege, Nils M., Marion Neumann, Christopher Morris, Kristian Kersting, & Petra Mutzel. (2017). A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels.. arXiv (Cornell University).11 indexed citations
Neumann, Marion, Shan Huang, Daniel Marthaler, & Kristian Kersting. (2015). pyGPs: a Python library for Gaussian process regression and classification. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 16(1). 2611–2616.17 indexed citations
Neumann, Marion, Roman Garnett, & Kristian Kersting. (2013). Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels. Asian Conference on Machine Learning. 357–372.4 indexed citations
13.
Neumann, Marion, et al.. (2013). Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping. Lirias (KU Leuven). 0–6.12 indexed citations
14.
Neumann, Marion, Roman Garnett, & Kristian Kersting. (2013). Coinciding Walk Kernels.1 indexed citations
15.
Schiegg, Martin, Marion Neumann, & Kristian Kersting. (2012). Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1002–1011.3 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.