Researchers from AC at the KI Institute will present a paper tackling key challenges in temporal graph learning. The work, titled “Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning”, will be presented at ECAI, a premier conference in machine learning.
Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning
Temporal graphs are key to understanding dynamic systems—from traffic flow to financial fraud. ETDNet introduces a dual-branch temporal graph neural network that decouples spatial (intra-frame) and temporal (inter-frame) edges.
This design avoids over-smoothing and allows effective long-range reasoning.
ETDNet improves driver-intention prediction (75.6% joint accuracy on Waymo) and illicit-transfer detection (88.1% F1 on Elliptic++), while outperforming transformers and memory-bank baselines with fewer parameters and faster training.
O. Mohammed, J. Pan, M. Nayyeri, D. Hernández, S. Staab. Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning. Proceedings of the 28th European Conference on Artificial Intelligence (ECAI 2025). https://arxiv.org/abs/2508.03251