Marion Neumann

2.1k citations
20 papers · 1.2k · 1 hit paper · h-index 11

Impact in

Papers in

Marion Neumann

20 papers receiving 1.1k citations

Marion Neumann's Hit Papers

An End-to-End Deep Learning Architecture for Graph Classification 2018 · 853 citations
8530+2+5Years since publication250500750

Peers

Marion Neumann
Comparison fields: 5 of 108
  • Artificial Intelligence 773
  • Statistical and Nonlinear Physics 265
  • Computer Vision and Pattern Recognition 304
  • Information Systems 152
  • Signal Processing 71
Replace Ziniu Hu with:
Ziniu Hu United States
Huifang Ma China
John Boaz Lee United States
Sungchul Kim United States
Yixin Liu China
Jinyin Chen China
Qimai Li Hong Kong
Hongyun Cai Singapore
Jilian Zhang China
Marion Neumann relative to Ziniu Hu United States Ziniu Hu's profile →
Citations per field
00.5×8.9×
Ziniu Hu · 1×
Citations per year

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).

Fields of papers citing papers by Marion Neumann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Marion Neumann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Marion Neumann Line = papers co-authored together Marion Neumann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
An End-to-End Deep Learning Architecture for Graph Classification
Hit paper breakdown →
2018853
2 2015118
3 201745
4 201428
5 200928
6 201826
7
pyGPs: a Python library for Gaussian process regression and classification
201517
8 201413
9
Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping
201312
10
A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels.
201711
11
A unifying view of explicit and implicit feature maps of graph kernels
201911
12 20218
13 20196
14 20115
15
Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels
20134
16
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data
20123
17 20231
18
Coinciding Walk Kernels
20131
19 20191
20 20061

About Marion Neumann

Marion Neumann is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing and Statistical and Nonlinear Physics, having authored 20 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Graph Theory and Algorithms (3 papers), Gaussian Processes and Bayesian Inference (3 papers), Data Management and Algorithms (2 papers), Plant Pathogens and Fungal Diseases (2 papers), Bayesian Modeling and Causal Inference (2 papers), Recommender Systems and Techniques (2 papers) and Artificial Intelligence in Healthcare and Education (2 papers). The work is most often cited by research in Artificial Intelligence (773 citations), Statistical and Nonlinear Physics (265 citations), Computer Vision and Pattern Recognition (304 citations), Information Systems (152 citations) and Signal Processing (71 citations). Marion Neumann has collaborated with scholars based in Germany, United States and Belgium. Frequent co-authors include Zhicheng Cui, Yixin Chen, Muhan Zhang, Kristian Kersting, Christian Bauckhage, Roman Garnett, Petra Mutzel, Nils M. Kriege, Zhao Xu and Daniel Schulz. Their work appears in journals such as Plant Pathology, Autonomous Robots, Machine Learning, Lirias (KU Leuven) and Proceedings of the AAAI Conference on Artificial Intelligence.

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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