lijfrank / openhine

An Open-Source Toolkit for Heterogeneous Information Network Embedding (HINE)

OpenHINE

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

Contributors：

DMGroup from BUPT: Tianyu Zhao, Meiqi Zhu, Jiawei Liu, Nian Liu, Guanyi Chu, Jiayue Liu, Xiao Wang, Cheng Yang, Linmei Hu, Chuan Shi.

Get started

Requirements and Installation

• Python version >= 3.6

• PyTorch version >= 1.4.0

• TensorFlow version >= 1.14

• Keras version >= 2.3.1

config/Usage

Input parameter

python train.py -m model_name -d dataset_name

e.g.

python train.py -m Metapath2vec -d acm

Model Setup

The model parameter could be modified in the file ( ./src/config.ini ).

• common parameter

​ --alpha: learning rate

​ --dim: dimension of output

​ --epoch: the number of iterations

​ etc...

• specific parameter

​ --metapath: the metapath selected

​ --neg_num: the number of negative samples

​ etc...

Datasets

If you want to train your own dataset, create the file (./dataset/your_dataset_name/edge.txt) and the format is as follows：

input: edge

​ 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.

Model

Available

DHNE

​ Structural Deep Embedding for Hyper-Networks

​ [DHNE AAAI 2018]

​ src code:https://github.com/tadpole/DHNE

HAN

​ Heterogeneous Graph Attention Network

​ [HAN WWW 2019]

​ src code:https://github.com/Jhy1993/HAN

HeGAN

​ Adversarial Learning on Heterogeneous Information Network

​ [HeGAN KDD 2019]

​ src code:https://github.com/librahu/HeGAN

HERec

​ Heterogeneous Information Network Embedding for Recommendation

​ [HERec TKDE 2018]

​ src code:https://github.com/librahu/HERec

*spec para:

​ metapath_list: pap|psp (split by "|")

HIN2vec

​ HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning

​ [HIN2Vec CIKM 2017]

​ src code:https://github.com/csiesheep/hin2vec

Metapath2vec

​ 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

MetaGrapth2vec

​ MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

​ [MetaGraph2Vec PAKDD 2018]

​ src code:https://github.com/daokunzhang/MetaGraph2Vec

RHINE

​ Relation Structure-Aware Heterogeneous Information Network Embedding

​ [RHINE AAAI 2019]

​ only supported in the Linux

​ src code:https://github.com/rootlu/RHINE 

Output

Test

python test.py -d dataset_name -m model_name -n file_name

The output embedding file name can be found in (./output/embedding/model_name/) .

e.g.

python test.py -d dblp -m HAN -n node.txt

Evaluation/Task

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.

Future work

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.