# OpenHINE **Repository Path**: chenwenhao18/OpenHINE ## Basic Information - **Project Name**: OpenHINE - **Description**: An Open-Source Toolkit for Heterogeneous Information Network Embedding (HINE) - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-09-03 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 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](http://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, Nian Liu, Jiawei Liu, Hongrui Liu, Guanyi Chu, Jiayue Liu, Jianan Zhao, Xiao Wang, Cheng Yang, 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 python train.py -m model_name -d dataset_name ``` e.g. ```python 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 ​ --num_workers:number of workers for dataset loading (It should be set to 0, if you are in trouble with Windows OS.) ​ 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. ###### Input: feature ​ number_of_nodes embedding_dim ​ node_name dim1 dim2 e.g. ``` 11246 2 a1814 0.06386946886777878 -0.04781734198331833 a0 ... ... ``` ## Model #### Available ##### [DHNE AAAI 2018] ​ Structural Deep Embedding for Hyper-Networks ​ src code:https://github.com/tadpole/DHNE ##### [HAN WWW 2019] ​ Heterogeneous Graph Attention Network ​ Add feature.txt into the input folder or set the parameter "featype": "adj", which means that you will use adjacency matrix as your feature. ​ src code:https://github.com/Jhy1993/HAN ##### [HeGAN KDD 2019] ​ Adversarial Learning on Heterogeneous Information Network ​ src code:https://github.com/librahu/HeGAN ##### [HERec TKDE 2018] ​ Heterogeneous Information Network Embedding for Recommendation ​ src code:https://github.com/librahu/HERec ###### *spec para: ​ metapath_list: pap|psp (split by "|") ##### [HIN2Vec CIKM 2017] ​ HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning ​ src code:https://github.com/csiesheep/hin2vec ##### [metapath2vec KDD 2017] ​ metapath2vec: Scalable Representation Learning for Heterogeneous Networks ​ 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 PAKDD 2018] ​ MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding ​ src code:https://github.com/daokunzhang/MetaGraph2Vec ##### [PTE KDD 2015] ​ PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks ​ src code:https://github.com/mnqu/PTE ##### [RHINE AAAI 2019] ​ Relation Structure-Aware Heterogeneous Information Network Embedding ​ only supported in the Linux ​ src code:https://github.com/rootlu/RHINE  ## Output #### Test ```python 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 python test.py -d dblp -m HAN -n node.txt ``` ###### output: embedding ​ number_of_nodes embedding_dim ​ node_name dim1 dim2 e.g. ``` 11246 2 a1814 0.06386946886777878 -0.04781734198331833 a0 ... ... ``` ## 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 | | PTE | 0.7624 | 0.7543 | 0.3781 | | 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 | | PTE | 0.9335 | 0.9301 | 0.3280 | | 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. Submit a issue or email to tyzhao@bupt.edu.cn.