Thorben Funke
Thorben Funke
L3S Research Center, Leibniz University Hannover
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Stochastic block models: A comparison of variants and inference methods
T Funke, T Becker
PloS one 14 (4), e0215296, 2019
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks
T Funke, M Khosla, M Rathee, A Anand
arXiv preprint arXiv:2105.08621, 2021
Complex networks of material flow in manufacturing and logistics: Modeling, analysis, and prediction using stochastic block models
T Funke, T Becker
Journal of Manufacturing Systems 56, 296-311, 2020
Releasing Graph Neural Networks with Differential Privacy Guarantees
IE Olatunji, T Funke, M Khosla
arXiv preprint arXiv:2109.08907, 2021
Statistical manifold embedding for directed graphs
T Funke, T Guo, A Lancic, N Antulov-Fantulin
8th International Conference on Learning Representations (ICLR 2020)(virtual), 2020
Stochastic block models as a modeling approach for dynamic material flow networks in manufacturing and logistics
T Funke, T Becker
Procedia CIRP 72, 539-544, 2018
Learnt Sparsification for Interpretable Graph Neural Networks
M Rathee, Z Zhang, T Funke, M Khosla, A Anand
arXiv preprint arXiv:2106.12920, 2021
Forecasting changes in material flow networks with stochastic block models
T Funke, T Becker
Procedia CIRP 81, 1183-1188, 2019
A Tool for an Analysis of the Dynamic Behavior of Logistic Systems with the Instruments of Complex Networks
T Funke, T Becker
International Conference on Dynamics in Logistics, 418-425, 2018
An Adaptive Clustering Approach for Accident Prediction
R Dadwal, T Funke, E Demidova
2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021
Analyzing and Predicting Material Flow Networks Using Stochastic Block Models and Statistical Graph Embeddings
T Funke
Universität Bremen, 2020
Machine Learning Methods for Prediction of Changes in Material Flow Networks
T Becker, T Funke
Procedia CIRP 93, 485-490, 2020
Systematisierung der Anforderungserhebung für die Incident-Auswertung bei einem Automobilzulieferer
T Funke
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