Peter Bloem

5.3k total citations
9 papers, 86 citations indexed

About

Peter Bloem is a scholar working on Molecular Biology, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Peter Bloem has authored 9 papers receiving a total of 86 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 5 papers in Artificial Intelligence and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Peter Bloem's work include Advanced Graph Neural Networks (3 papers), Neural dynamics and brain function (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Peter Bloem is often cited by papers focused on Advanced Graph Neural Networks (3 papers), Neural dynamics and brain function (2 papers) and EEG and Brain-Computer Interfaces (2 papers). Peter Bloem collaborates with scholars based in Netherlands and United States. Peter Bloem's co-authors include Victor de Boer, Steven de Rooij, Paul Groth, Gerben Klaas Dirk de Vries, Jennifer R. Ramautar, K. Anton Feenstra, Hilgo Bruining, Arthur-Ervin Avrămiea, Klaus Linkenkaer‐Hansen and Simon J. Houtman and has published in prestigious journals such as Scientific Reports, Data Mining and Knowledge Discovery and Lecture notes in computer science.

In The Last Decade

Peter Bloem

9 papers receiving 80 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter Bloem Netherlands	6	47	20	12	12	10	9	86
Ehsan Hajiramezanali United States	6	58 1.2×	18 0.9×	11 0.9×	7 0.6×	11 1.1×	8	96
Paul Komarek United States	4	78 1.7×	10 0.5×	26 2.2×	7 0.6×	26 2.6×	6	115
Ines Chami United States	3	72 1.5×	17 0.8×	19 1.6×	31 2.6×	13 1.3×	6	111
Zheng Fang China	5	45 1.0×	10 0.5×	9 0.8×	3 0.3×	20 2.0×	14	93
Michel Crampes France	5	47 1.0×	14 0.7×	24 2.0×	6 0.5×	37 3.7×	16	97
Rati Gelashvili United States	6	26 0.6×	12 0.6×	10 0.8×	5 0.4×	16 1.6×	15	98
John D. Eblen United States	5	26 0.6×	47 2.4×	12 1.0×	4 0.3×	9 0.9×	7	113
James Hammerton Netherlands	5	208 4.4×	25 1.3×	27 2.3×	33 2.8×	12 1.2×	7	232
Jinheon Baek South Korea	6	142 3.0×	14 0.7×	30 2.5×	9 0.8×	38 3.8×	19	194
Ofer Meshi United States	8	107 2.3×	36 1.8×	4 0.3×	6 0.5×	29 2.9×	16	163

Countries citing papers authored by Peter Bloem

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Peter Bloem

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

All Works

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9 of 9 papers shown

1.

Avrămiea, Arthur-Ervin, et al.. (2023). Robin’s Viewer: Using deep-learning predictions to assist EEG annotation. Frontiers in Neuroinformatics. 16. 1025847–1025847. 3 indexed citations

2.

Bloem, Peter, et al.. (2022). ProteinGLUE multi-task benchmark suite for self-supervised protein modeling. Scientific Reports. 12(1). 16047–16047. 8 indexed citations

3.

Houtman, Simon J., Jennifer R. Ramautar, Huibert D. Mansvelder, et al.. (2022). Improved Manual Annotation of EEG Signals through Convolutional Neural Network Guidance. eNeuro. 9(5). ENEURO.0160–22.2022. 6 indexed citations

4.

Bloem, Peter, et al.. (2022). Relational graph convolutional networks: a closer look. PeerJ Computer Science. 8. e1073–e1073. 14 indexed citations

5.

Bloem, Peter, et al.. (2021). kgbench: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning. Lecture notes in computer science. 614–630. 2 indexed citations

6.

Bloem, Peter & Steven de Rooij. (2020). Large-scale network motif analysis using compression. Data Mining and Knowledge Discovery. 34(5). 1421–1453. 9 indexed citations

7.

Bloem, Peter, et al.. (2017). The knowledge graph as the default data model for learning on heterogeneous knowledge. VU Research Portal. 1(1-2). 39–57. 35 indexed citations

8.

Bloem, Peter & Gerben Klaas Dirk de Vries. (2014). Machine learning on linked data, a position paper. UvA-DARE (University of Amsterdam). 64–68. 4 indexed citations

9.

Bloem, Peter, et al.. (2014). Simplifying RDF Data for Graph-Based Machine Learning.. VU Research Portal. 5 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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