This is the implementation for the NeurIPS 2025 Conference paper
DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion.
Jin Li, Zezhong Ding, Xike Xie*
Knowledge graphs (KGs) are vital for enabling knowledge reasoning across various domains. Recent KG reasoning methods that integrate both global and local information have achieved promising results. However, existing methods often suffer from score over-smoothing, which blurs the distinction between correct and incorrect answers and hinders reasoning effectiveness. To address this, we propose DuetGraph, a coarse-to-fine KG reasoning mechanism with dual-pathway global-local fusion. DuetGraph tackles over-smoothing by segregating — rather than stacking — the processing of local (via message passing) and global (via attention) information into two distinct pathways, preventing mutual interference and preserving representational discrimination. In addition, DuetGraph introduces a coarse-to-fine optimization, which partitions entities into high- and low-score subsets. This strategy narrows the candidate space and sharpens the score gap between the two subsets, thereby alleviating over-smoothing and enhancing inference quality. Extensive experiments on various datasets demonstrate that DuetGraph achieves state-of-the-art (SOTA) performance, with up to 8.7% improvement in reasoning quality and a 1.8× acceleration in training efficiency.
- Python 3.9.21
- Pytorch 2.6.0
- CUDA 12.1
- pytorch-lightning 1.9.1
- torch-geometric 2.4.0
- torchmetrics 0.11.4
- einops 0.7.0
- numpy 1.24.1
- scipy 1.11.4
- scikit-learn 1.6.1
- tqdm 4.67.1
If you used our work or found it helpful, please kindly cite our paper:
@inproceedings{li2025duetgraph,
title={DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion},
author={Jin Li and Zezhong Ding and Xike Xie},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025}
}