NSHE

NSHE[IJCAI 2020]

Paper: Network Schema Preserving Heterogeneous Information Network Embedding

Code from author: https://github.com/Andy-Border/NSHE

How to run

Clone the Openhgnn-DGL

python main.py -m NSHE -t node_classification -d acm4NSHE -g 0 --use_best_config

If you do not have gpu, set -gpu -1.

Performance

Node classification for acm4NSHE

Node classification	Macro-F1	Micro-F1
paper	83.27	84.12
OpenHGNN	84.78	84.95

Dataset

We process the acm dataset given by NSHE. It saved as dgl.heterograph and can be loaded by dgl.load_graphs

You can download the dataset by

wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dataset/acm4NSHE.zip

Or run the code mentioned above and it will download automaticlly.

Description: acm4NSHE

TrainerFlow: NSHETrainer

• Model:Encoder
  • GCN
  • Context-encoder
• Preserving Pairwise Proximity
  • Sample positive edge and negative edge
• Preserving Network Schema Proximity
  • Network Schema Instance Sampling

Note: [TODO] We will use the dataloader to combine the two sampler without storing the temporal file and use mini-batch trainer to improve the training efficiency.

Hyper-parameter specific to the model

num_e_neg = 2 # number of negative edges
num_ns_neg = 3 # number of negative schemas

Best config can be found in best_config

More

Contirbutor

Tianyu Zhao[GAMMA LAB]

If you have any questions,

Submit an issue or email to [email protected].