Source code for AAAI 2019 paper "Relation Structure-Aware Heterogeneous Information Network Embedding"
-
Python 2.7
-
numpy
-
scipy
-
PyTorch (0.3.0)
-
My machine with two GPUs (NVIDIA GTX-1080 *2) and two CPUs (Intel Xeon E5-2690 * 2)
RHINE/
├── code
│ ├── config
│ │ ├── Config.py：configs for model.
│ │ └──_init_.py
│ ├── evaluation.py: evaluate the performance of learned embeddings w.r.t clustering and classification
│ ├── models
│ │ ├── _init_.py
│ │ ├── Model.py: the super model with some functions
│ │ └── RHINE.py: our model
│ ├── preData
│ │ └── dblpDataHelper.py: data preparation for our mode
│ ├── release
│ │ ├── Sample_ARs.so: sampling with dll
│ │ └── Sample_IRs.so
│ └── trainRHINE.py: train model
├── data
│ └── dblp
│ ├── node2id.txt: the first line is the number of nodes, (node_type+node_name, node_id)
│ ├── paper_label.txt: (node_name, label)
│ ├── relation2id.txt: the first line is the number of relations, (relation_name, relation_id)
│ ├── train2id_apc.txt: (node1_id, node2_id, relation_id, weight)
│ ├── train2id_pc.txt
│ ├── train2id_ap.txt
│ ├── train2id_pt.txt
│ ├── train2id_apt.txt
│ ├── train2id_ARs.txt: the first line is the number of ARs triples, (node1_id, node2_id, relation_id, weight)
│ └── train2id_IRs.txt
├── README.md
└── res
└── dblp
└── embedding.vec.ap_pt_apt+pc_apc.json: the learned embeddings
@inproceedings{Yuanfu2019RHINE,
title={Relation Structure-Aware Heterogeneous Information Network Embedding},
author={Yuanfu Lu, Chuan Shi, Linmei Hu, Zhiyuan Liu.}
booktitle={Proceedings of AAAI},
year={2019}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent