Maximilian Nickel
- Artificial Intelligence top 0.5%
- Management Science and Operations Research top 1%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Co-authors
- Volker TrespHans‐Peter KriegelKevin MurphyEvgeniy GabrilovichLorenzo RosascoTomaso PoggioLéon BottouDavid López-Paz
- Topics
- Advanced Graph Neural Networks (8 papers)Topic Modeling (6 papers)Tensor decomposition and applications (5 papers)
- Partner nations
- United StatesGermanyItaly
In The Last Decade
Maximilian Nickel
19 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Artificial Intelligence 2.1k
- Management Science and Operations Research 470
- Information Systems 325
- Computer Vision and Pattern Recognition 302
- Molecular Biology 300
Countries citing papers authored by Maximilian Nickel
This map shows the geographic impact of Maximilian Nickel'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 Maximilian Nickel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian Nickel more than expected).
Fields of papers citing papers by Maximilian Nickel
This network shows the impact of papers produced by Maximilian Nickel. 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 Maximilian Nickel. The network helps show where Maximilian Nickel may publish in the future.
Co-authorship network of co-authors of Maximilian Nickel
This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Nickel. A scholar is included among the top collaborators of Maximilian Nickel 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 Maximilian Nickel. Maximilian Nickel 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 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | Learning Complex Geometric Structures from Data with Deep Riemannian Manifolds | 1 |
| 6 | 39 | |
| 7 | 25 | |
| 8 | 28 | |
| 9 | Fast Linear Model for Knowledge Graph Embeddings. | 2 |
| 10 | Holographic Embeddings of Knowledge Graphsbreakdown → | 284 |
| 11 | A Review of Relational Machine Learning for Knowledge Graphs From Multi-Relational Link Prediction to Automated Knowledge Graph Construction | 35 |
| 12 | Reducing the Rank in Relational Factorization Models by Including Observable Patterns | 31 |
| 13 | 2 | |
| 14 | 14 | |
| 15 | 13 | |
| 16 | 19 | |
| 17 | 196 | |
| 18 | Link prediction in multi-relational graphs using additive models | 8 |
| 19 | A Three-Way Model for Collective Learning on Multi-Relational Databreakdown → | 905 |
| 20 | 1 |
About Maximilian Nickel
Maximilian Nickel is a scholar working on Computational Mathematics, Artificial Intelligence and Management Science and Operations Research, having authored 21 papers that have together received 2.4k indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (8 papers), Topic Modeling (6 papers) and Tensor decomposition and applications (5 papers). The work is most often cited by research in Computational Mathematics (112 citations), Artificial Intelligence (2.1k citations) and Management Science and Operations Research (470 citations). Maximilian Nickel has collaborated with scholars based in United States, Germany and Italy. Frequent co-authors include Volker Tresp, Hans‐Peter Kriegel, Kevin Murphy, Evgeniy Gabrilovich, Lorenzo Rosasco, Tomaso Poggio, Léon Bottou, David López-Paz, Matthew Le and Qi Liu. Their work appears in journals such as Nature Communications, Proceedings of the IEEE and Semantic Web.
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.