Rick Ratzel

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Demo Data from EDBT 2026 / reviewers in the wild / expert

dblp:120/0966 · DBLP profile ↗

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1ranked-venue papers

0as first author

1since 2021 · last 2022

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Graph learning · 70% Efficient and distributed learning · 30%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Graph learning › graph neural network fraud detection	0.6	1	2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow · KDD 2022
Machine learning › Graph learning graph neural network training	0.6	1	2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow · KDD 2022
Machine learning › Graph learning › graph neural network › heterogeneous graph neural network relational graph convolutional network	0.6	1	2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow · KDD 2022
Machine learning › Efficient and distributed learning › efficient training training acceleration	0.6	1	2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow · KDD 2022
Machine learning › Efficient and distributed learning › hardware acceleration GPU training	0.2	1	2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow · KDD 2022

Methods — techniques the papers use, named apart from their topics

graph neural network · 0.6gradient-boosted decision trees · 0.6RGCN · 0.6

Year	Publication	Venue	Position
2022	Accelerated GNN Training with DGL and RAPIDS cuGraph in a Fraud Detection Workflow abstract Graph Neural Networks (GNNs) have gained the interest of industry with Relational Graph Convolutional Networks (R-GCNs) showing promise for fraud detection. Taking existing workflows that leverage graph features to train a gradient boosted decision tree (GBDT) and replacing the graph features with GNN produced embedding achieves an increase in accuracy. However, recent work has shown that the combination of graph attributes with GNN embeddings provides the biggest lift in accuracy. Bradley Rees, Xiaoyun Wang 0001, Joe Eaton, Onur Yilmaz, Rick Ratzel, Dominique LaSalle	KDD	5