RED-GNN

This repository contains the official implementation of our paper Neural knowledge graph reasoning with relational digraph, published in the Artificial Intelligence Journal.

Instructions

A quick instruction is given for readers to reproduce the whole process.

Requirements

• pytorch 1.9.1+cu102
• torch_scatter 2.0.9

Static KG

This part of the code contains the Transductive and Inductive settings of Static KG.

cd Static

For transductive reasoning

cd transductive
python -W ignore train.py --data_path=data/YAGO

For inductive reasoning

cd inductive
python -W ignore train.py --data_path=data/fb237_v2

Data splition in transductive setting

We follow the rule mining methods, i.e., Neural-LP and DRUM, to randomly split triplets in the original train.txt file into two files facts.txt and train.txt with ratio 3:1. This step is to make sure that the query triplets will not be leaked in the fact triplets used in RED-GNN. Empirically, increasing the ratio of facts, e.g. from 3:1 to 4:1, will lead to better performance.

Temporal KG

This project provides implementations for temporal knowledge graph reasoning tasks under both interpolation and extrapolation settings. It supports datasets such as ICEWS14, ICEWS05-15, Wikidata11k, and YAGO. The original folder T-GAP-RED has been renamed to interpolation, and T-xERTE-RED has been renamed to extrapolation.

How to Run

Interpolation (in interpolation/)

• ICEWS14

  python interpolation/main.py
  

• ICEWS05-15

  python interpolation/main_icews05-15.py
  

• Wikidata11k

  python interpolation/main_wikidata11k.py
  

Extrapolation (in extrapolation/)

• ICEWS14_forecasting

  python extrapolation/main.py \
    --warm_start_time 48 \
    --dataset ICEWS14_forecasting \
    --epoch 40 \
    --device 0 \
    --batch_size 2
  

• YAGO

  python extrapolation/main.py \
    --warm_start_time 48 \
    --dataset YAGO \
    --epoch 40 \
    --device 7 \
    --batch_size 2
  

Citation

If you find our method useful, please cite our paper:

@article{Zhang2026REDGNN,
  title   = {Neural knowledge graph reasoning with relational digraph},
  author  = {Zhang, Yongqi and Qiu, Haiquan and Liu, Shuzhi and Du, Enjun and Yao, Quanming},
  journal = {Artificial Intelligence},
  volume  = {355},
  pages   = {104520},
  year    = {2026},
  publisher = {Elsevier},
  doi     = {10.1016/j.artint.2026.104520},
  url     = {https://doi.org/10.1016/j.artint.2026.104520}
}