| Stochastic block models: A comparison of variants and inference methods T Funke, T Becker PloS one 14 (4), e0215296, 2019 | 92 | 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 | 87* | 2021 |
| Releasing Graph Neural Networks with Differential Privacy Guarantees IE Olatunji, T Funke, M Khosla arXiv preprint arXiv:2109.08907, 2021 | 57 | 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 | 30 | 2020 |
| Private graph extraction via feature explanations IE Olatunji, M Rathee, T Funke, M Khosla arXiv preprint arXiv:2206.14724, 2022 | 17 | 2022 |
| BAGEL: A Benchmark for Assessing Graph Neural Network Explanations M Rathee, T Funke, A Anand, M Khosla arXiv preprint arXiv:2206.13983, 2022 | 17 | 2022 |
| An Adaptive Clustering Approach for Accident Prediction R Dadwal, T Funke, E Demidova 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 9 | 2021 |
| Learnt Sparsification for Interpretable Graph Neural Networks M Rathee, Z Zhang, T Funke, M Khosla, A Anand arXiv preprint arXiv:2106.12920, 2021 | 9 | 2021 |
| Hard masking for explaining graph neural networks, 2021 T Funke, M Khosla, A Anand URL https://openreview. net/forum, 2020 | 8 | 2020 |
| 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 | 7* | 2020 |
| The smashHitCore ontology for GDPR-compliant sensor data sharing in smart cities A Kurteva, TR Chhetri, A Tauqeer, R Hilscher, A Fensel, K Nagorny, ... Sensors 23 (13), 6188, 2023 | 6 | 2023 |
| 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 | 6 | 2018 |
| Forecasting changes in material flow networks with stochastic block models T Funke, T Becker Procedia CIRP 81, 1183-1188, 2019 | 4 | 2019 |
| RE-Trace: Re-Identification of Modified GPS Trajectories S Schestakov, S Gottschalk, T Funke, E Demidova ACM Transactions on Spatial Algorithms and Systems, 2024 | 3 | 2024 |
| W-trace: robust and effective watermarking for GPS trajectories R Dadwal, T Funke, M Nüsken, E Demidova Proceedings of the 30th International Conference on Advances in Geographic …, 2022 | 3 | 2022 |
| Using Vehicle Data to Enhance Prediction of Accident-Prone Areas KS Wowo, R Dadwal, T Graen, A Fiege, M Nolting, W Nejdl, E Demidova, ... 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 3 | 2022 |
| 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 | 3 | 2018 |
| Machine Learning Methods for Prediction of Changes in Material Flow Networks T Becker, T Funke Procedia CIRP 93, 485-490, 2020 | 1 | 2020 |
| Ego-Vehicle Speed Prediction with Walk-Ahead P Matesanz, N Tempelmeier, M Nolting, T Funke 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | | 2022 |
| Analyzing and Predicting Material Flow Networks Using Stochastic Block Models and Statistical Graph Embeddings T Funke Universität Bremen, 2020 | | 2020 |
