Awesome
DHNE
This is an implementation of "Structural Deep Embedding for Hyper-Networks"(AAAI 2018).
Requirements
python >= 2.7.0
scipy >= 0.19.1
numpy >= 1.13.1
tensorflow >= 1.0.0
Keras >= 2.0.8
Usage
Example Usage
python src/hypergraph_embedding.py --data_path data/GPS --save_path result/GPS -s 16 16 16
Full Command List
usage: hyper-network embedding [-h] [--data_path DATA_PATH]
[--save_path SAVE_PATH]
[-s EMBEDDING_SIZE EMBEDDING_SIZE EMBEDDING_SIZE]
[--prefix_path PREFIX_PATH]
[--hidden_size HIDDEN_SIZE]
[-e EPOCHS_TO_TRAIN] [-b BATCH_SIZE]
[-lr LEARNING_RATE] [-a ALPHA]
[-neg NUM_NEG_SAMPLES] [-o OPTIONS]
[--seed SEED]
optional arguments:
-h, --help show this help message and exit
--data_path DATA_PATH
Directory to load data.
--save_path SAVE_PATH
Directory to save data.
-s EMBEDDING_SIZE EMBEDDING_SIZE EMBEDDING_SIZE,
--embedding_size EMBEDDING_SIZE EMBEDDING_SIZE EMBEDDING_SIZE
The embedding dimension size
--prefix_path PREFIX_PATH
.
--hidden_size HIDDEN_SIZE
The hidden full connected layer size
-e EPOCHS_TO_TRAIN, --epochs_to_train EPOCHS_TO_TRAIN
Number of epoch to train. Each epoch processes the
training data once completely
-b BATCH_SIZE, --batch_size BATCH_SIZE
Number of training examples processed per step
-lr LEARNING_RATE, --learning_rate LEARNING_RATE
initial learning rate
-a ALPHA, --alpha ALPHA
radio of autoencoder loss
-neg NUM_NEG_SAMPLES, --num_neg_samples NUM_NEG_SAMPLES
Neggative samples per training example
-o OPTIONS, --options OPTIONS
options files to read, if empty, stdin is used
--seed SEED random seed
Cite
If you find this code useful, please cite our paper:
@article{tu2017structural,
title={Structural Deep Embedding for Hyper-Networks},
author={Tu, Ke and Cui, Peng and Wang, Xiao and Wang, Fei and Zhu, Wenwu},
journal={arXiv preprint arXiv:1711.10146},
year={2017}
}