| Scattering gcn: Overcoming oversmoothness in graph convolutional networks Y Min, F Wenkel, G Wolf Advances in neural information processing systems 33, 14498-14508, 2020 | 134 | 2020 |
| Towards foundational models for molecular learning on large-scale multi-task datasets D Beaini, S Huang, JA Cunha, Z Li, G Moisescu-Pareja, O Dymov, ... arXiv preprint arXiv:2310.04292, 2023 | 25 | 2023 |
| Can hybrid geometric scattering networks help solve the maximum clique problem? Y Min, F Wenkel, M Perlmutter, G Wolf Advances in Neural Information Processing Systems 35, 22713-22724, 2022 | 21 | 2022 |
| Taxonomy of benchmarks in graph representation learning R Liu, S Cantürk, F Wenkel, S McGuire, X Wang, A Little, L O’Bray, ... Learning on Graphs Conference, 6: 1-6: 25, 2022 | 17 | 2022 |
| Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks F Wenkel, Y Min, M Hirn, M Perlmutter, G Wolf arXiv preprint arXiv:2201.08932, 2022 | 16 | 2022 |
| Geometric scattering attention networks Y Min, F Wenkel, G Wolf ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 14 | 2021 |
| Yimeng Min, Matthew Hirn, Michael Perlmutter, and Guy Wolf. Overcoming oversmoothness in graph convolutional networks via hybrid scattering networks F Wenkel arXiv preprint arXiv:2201.08932, 2022 | 13 | 2022 |
| Learnable filters for geometric scattering modules A Tong, F Wenkel, D Bhaskar, K Macdonald, J Grady, M Perlmutter, ... IEEE Transactions on Signal Processing, 2024 | 11 | 2024 |
| Data-driven learning of geometric scattering networks A Tong, F Wenkel, K MacDonald, S Krishnaswamy, G Wolf arXiv preprint arXiv:2010.02415, 2020 | 7 | 2020 |
| On the Scalability of GNNs for Molecular Graphs M Sypetkowski, F Wenkel, F Poursafaei, N Dickson, K Suri, P Fradkin, ... arXiv preprint arXiv:2404.11568, 2024 | 5 | 2024 |
| Inferring dynamic regulatory interaction graphs from time series data with perturbations D Bhaskar, DS Magruder, M Morales, E De Brouwer, A Venkat, F Wenkel, ... Learning on Graphs Conference, 22: 1-22: 21, 2024 | 4 | 2024 |
| Towards a General GNN Framework for Combinatorial Optimization F Wenkel, S Cantürk, M Perlmutter, G Wolf arXiv preprint arXiv:2405.20543, 2024 | 2 | 2024 |
| Pretrained language models to solve graph tasks in natural language F Wenkel, G Wolf, B Knyazev ICML 2023 Workshop on Structured Probabilistic Inference {\&} Generative …, 2023 | 2 | 2023 |
| Towards a taxonomy of graph learning datasets R Liu, S Cantürk, F Wenkel, D Sandfelder, D Kreuzer, A Little, S McGuire, ... arXiv preprint arXiv:2110.14809, 2021 | 2 | 2021 |
| How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval P Fradkin, P Azadi, K Suri, F Wenkel, A Bashashati, M Sypetkowski, ... arXiv preprint arXiv:2409.08302, 2024 | | 2024 |
| Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs F Wenkel, S Cantürk, S Horoi, M Perlmutter, G Wolf The Third Learning on Graphs Conference, 0 | | |
| Molphenix: A Multimodal Foundation Model for PhenoMolecular Retrieval P Fradkin, PA Moghadam, K Suri, F Wenkel, M Sypetkowski, D Beaini Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities …, 0 | | |
| Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks–Supplement Y Min, F Wenkel, G Wolf | | |
| Scattering GCN: Overcoming Oversmoothness in Graph Conv. Networks Y Min, F Wenkel, G Wolf | | |
| TUM Data Innovation Lab S Kathuria, J Zhang, F Wenkel, P Kim, CML Vuaille | | |
