This is an open-source toolkit for Heterogeneous Information Network Embedding(OpenHINE) with version 0.1. We can train and test the model more easily. It provides implementations of many popular models, including: DHNE, HAN, HeGAN, HERec, HIN2vec, Metapath2vec, MetaGraph2vec, RHINE. More materials can be found in www.shichuan.org.
convenience provided:
- easy to train and evaluate
- able to extend new/your datasets and models
- the latest model available: HAN、HeGAN and so on
DMGroup from BUPT: Tianyu Zhao, Meiqi Zhu, Jiawei Liu, Nian Liu, Guanyi Chu, Jiayue Liu, Xiao Wang, Cheng Yang, Linmei Hu, Chuan Shi.
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Python version >= 3.6
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PyTorch version >= 1.4.0
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TensorFlow version >= 1.14
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Keras version >= 2.3.1
python train.py -m model_name -d dataset_namee.g.
python train.py -m Metapath2vec -d acmThe model parameter could be modified in the file ( ./src/config.ini ).
--alpha: learning rate
--dim: dimension of output
--epoch: the number of iterations
etc...
--metapath: the metapath selected
--neg_num: the number of negative samples
etc...
If you want to train your own dataset, create the file (./dataset/your_dataset_name/edge.txt) and the format is as follows：
src_node_id dst_node_id edge_type weight
e.g.
19 7 p-c 2
19 7 p-a 1
11 0 p-c 1
0 11 c-p 1
PS：The input graph is directed and the undirected needs to be transformed into directed graph.
Structural Deep Embedding for Hyper-Networks
[DHNE AAAI 2018]
src code:https://github.com/tadpole/DHNE
Heterogeneous Graph Attention Network
[HAN WWW 2019]
src code:https://github.com/Jhy1993/HAN
Adversarial Learning on Heterogeneous Information Network
[HeGAN KDD 2019]
src code:https://github.com/librahu/HeGAN
Heterogeneous Information Network Embedding for Recommendation
[HERec TKDE 2018]
src code:https://github.com/librahu/HERec
metapath_list: pap|psp (split by "|")
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
[HIN2Vec CIKM 2017]
src code:https://github.com/csiesheep/hin2vec
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
[metapath2vec KDD 2017]
src code:https://ericdongyx.github.io/metapath2vec/m2v.html
the python version implemented by DGL:https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
[MetaGraph2Vec PAKDD 2018]
src code:https://github.com/daokunzhang/MetaGraph2Vec
Relation Structure-Aware Heterogeneous Information Network Embedding
[RHINE AAAI 2019]
only supported in the Linux
src code:https://github.com/rootlu/RHINE
python test.py -d dataset_name -m model_name -n file_nameThe output embedding file name can be found in (./output/embedding/model_name/) .
e.g.
python test.py -d dblp -m HAN -n node.txt| ACM dataset | Micro-F1 | Macro-F1 | NMI |
|---|---|---|---|
| DHNE | 0.7201 | 0.7007 | 0.3280 |
| HAN | 0.8401 | 0.8362 | 0.4241 |
| HeGAN | 0.8308 | 0.8276 | 0.4335 |
| HERec | 0.8308 | 0.8304 | 0.3618 |
| HIN2vec | 0.8458 | 0.8449 | 0.4148 |
| Metapath2vec(PAP) | 0.7823 | 0.7725 | 0.2828 |
| MetaGraph2vec | 0.8085 | 0.8019 | 0.5095 |
| RHINE | 0.7699 | 0.7571 | 0.3970 |
| DBLP dataset | Micro-F1 | Macro-F1 | NMI |
|---|---|---|---|
| DHNE | --- | --- | --- |
| HAN | 0.8325 | 0.8141 | 0.3415 |
| HeGAN | 0.9414 | 0.9364 | 0.7898 |
| HERec | 0.9249 | 0.9214 | 0.3412 |
| HIN2vec | 0.9495 | 0.9460 | 0.3924 |
| Metapath2vec(APCPA) | 0.9483 | 0.9448 | 0.7786 |
| MetaGraph2vec | 0.9138 | 0.9093 | 0.6136 |
| RHINE | 0.9360 | 0.9316 | 0.7356 |
HAN uses the dataset without features.
Note that OpenHINE is just version 0.1 and still actively under development, so feedback and contributions are welcome. Feel free to submit your questions as a issue.
In the future, we will contain more models and tasks. We use the assorted deep learning framework, so we want to unify the model with PyTorch. If you have a demo of the above model with PyTorch or want your method added into our toolkit, contract us please.