| De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs L Qarkaxhija, V Perri, I Scholtes Learning on Graphs, Proceedings for Machine Learning Research, 2022 | 4 | 2022 |
| One Graph to Rule them All: Using NLP and Graph Neural Networks to analyse Tolkien's Legendarium V Perri, L Qarkaxhija, A Zehe, A Hotho, I Scholtes Third International Conference on Computational Humanities Research (CHR2022), 2022 | 2 | 2022 |
| Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs J von Pichowski, V Perri, L Qarkaxhija, I Scholtes arXiv preprint arXiv:2406.16552, 2024 | | 2024 |
| Link Prediction with Untrained Message Passing Layers L Qarkaxhija, AE Wegner, I Scholtes arXiv preprint arXiv:2406.16687, 2024 | | 2024 |
| 4.6 Visualisation and Interpretability of Higher-Order Networks C Tan, D Edler, A Eriksson, DF Gleich, L Qarkaxhija Higher-Order Graph Models: From Theoretical Foundations to Machine Learning, 168, 0 | | |
Ingo ScholtesProfessor of Machine Learning for Complex Networks at University of Würzburg, SNSF Professor at UZH在 uni-wuerzburg.de 的电子邮件经过验证
