Peter Bloem
Impact in
- Artificial Intelligence top 0.5%
- Advanced Graph Neural Networks
- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
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- Complex Network Analysis Techniques
Papers in
-
- Semantic Web and Ontologies 7
- Advanced Graph Neural Networks 5
- Topic Modeling 4
- Natural Language Processing Techniques 2
- Co-authors
- Rianne van den Berg (1 shared paper)Max Welling (1 shared paper)Michael Schlichtkrull (1 shared paper)Ivan Titov (1 shared paper)Thomas Kipf (1 shared paper)Victor de Boer (3 shared papers)Steven de Rooij (4 shared papers)Guido van Wingen (1 shared paper)
- Journals
- Lecture notes in computer science (11 papers)Scientific Reports (1 paper)Frontiers in Neuroinformatics (1 paper)PeerJ Computer Science (1 paper)eNeuro (1 paper)
- Partner nations
- NetherlandsUnited KingdomItaly
In The Last Decade
Peter Bloem
17 papers receiving 3.2k citations
Peter Bloem's Hit Papers
Peers
Comparison fields: 5 of 129
- Artificial Intelligence 2.5k
- Statistical and Nonlinear Physics 401
- Computer Vision and Pattern Recognition 559
- Management Science and Operations Research 322
- Information Systems 574
Countries citing papers authored by Peter Bloem
This map shows the geographic impact of Peter Bloem'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 Peter Bloem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Bloem more than expected).
Fields of papers citing papers by Peter Bloem
This network shows the impact of papers produced by Peter Bloem. 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 Peter Bloem. The network helps show where Peter Bloem may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Bloem, 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 | Modeling Relational Data with Graph Convolutional Networks Hit paper breakdown → | 2018 | 3094 |
| 2 | 2017 | 46 | |
| 3 | 2019 | 35 | |
| 4 | 2022 | 18 | |
| 5 | 2017 | 15 | |
| 6 | 2020 | 12 | |
| 7 | 2014 | 10 | |
| 8 | 2022 | 8 | |
| 9 | 2022 | 7 | |
| 10 | 2016 | 7 | |
| 11 | 2015 | 7 | |
| 12 | Simplifying RDF Data for Graph-Based Machine Learning. | 2014 | 5 |
| 13 | Machine learning on linked data, a position paper | 2014 | 4 |
| 14 | 2023 | 3 | |
| 15 | 2015 | 3 | |
| 16 | 2021 | 2 | |
| 17 | 2021 | 1 | |
| 18 | 2024 | 0 | |
| 19 | 2023 | 0 |
About Peter Bloem
Peter Bloem is a scholar working on Artificial Intelligence, Molecular Biology, Management Science and Operations Research, Information Systems and Cognitive Neuroscience, having authored 19 papers that have together received 3.3k indexed citations. Recurring topics across this work include Semantic Web and Ontologies (7 papers), Advanced Graph Neural Networks (5 papers), Data Quality and Management (4 papers), Topic Modeling (4 papers), EEG and Brain-Computer Interfaces (3 papers), Benford’s Law and Fraud Detection (2 papers), Computability, Logic, AI Algorithms (2 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (2.5k citations), Statistical and Nonlinear Physics (401 citations), Computer Vision and Pattern Recognition (559 citations), Management Science and Operations Research (322 citations) and Information Systems (574 citations). Peter Bloem has collaborated with scholars based in Netherlands, United Kingdom and Italy. Frequent co-authors include Rianne van den Berg, Max Welling, Michael Schlichtkrull, Ivan Titov, Thomas Kipf, Victor de Boer, Steven de Rooij, Guido van Wingen, Rajat M. Thomas and Paul Groth. Their work appears in journals such as Lecture notes in computer science, Scientific Reports, Frontiers in Neuroinformatics, PeerJ Computer Science and eNeuro.
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.