HIN-Align
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Bachelor degree research on heterogeneous information network alignment.
HIN-Align
Bachelor degree research on heterogeneous information network alignment. Extended work of GCN-Align.
Datasets are from JAPE and IONE.
Environment
- python>=3.5
- tensorflow>=1.10.1
- scipy>=1.1.0
- networkx>=2.2
Test
unzip dbp15k.zip
chmod +x test.sh
./test.sh
The pre-trained results are in the res/ folder. If you don't want to train by yourself, just see the files in it.
For social network, run:
python train_sn.py --seed 5
For automatically train weights:
python train_auto.py
Citation
Please politely cite our work as follows:
Zhichun Wang, Qingsong Lv, Xiaohan Lan, Yu Zhang. Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. In: EMNLP 2018.
TODO
- [x] Change a_ij to sigmoid(a_ij)
- [x] Combine with TransE (KG) or DeepWalk (SN)
- [ ] Combine with MT
- [x] Social Network Alignment
- [ ] Iterative or Bootstrapping
- [ ] Use faiss to improve evaluation speed
- [ ] Dimension Reduction or other ways of combination
- [x] Automatic training for hybrid weights
- [ ] Batched training for GCN
- [ ] Try other GNN models