BioNEV (Biomedical Network Embedding Evaluation)

1. Introduction

This repository contains source code and datasets for paper "Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations" (under review). This work aims to systematically evaluate recent advanced graph embedding techniques on biomedical tasks. We compile 5 benchmark datasets for 4 biomedical prediction tasks (see paper for details) and use them to evaluate 11 representative graph embedding methods selected from different categories:

5 matrix factorization-based: Laplacian Eigenmap, SVD, Graph Factorization, HOPE, GraRep
3 random walk-based: DeepWalk, node2vec, struc2vec
3 neural network-based: LINE, SDNE, GAE

The code can also be applied to graphs in other domains (e.g., social networks, citation networks). More experimental details can be found in Supplementary Materials.

2. Dataset

Datasets used in the paper:

CTD DDA : a drug-disease association graph extracted from Comparative Toxicogenomics Database
NDFRT DDA : a drug-disease association graph extracted from UMLS National Drug File
DrugBank DDi : a drug-drug interaction graph extracted from DrugBank database
STRING PPI : a protein-protein interaction graph extracted from STRING database
Clin Term COOC : a medical term-term co-occurrence graph from (Finlayson et al., 2014) [source data], [paper]

Statistics:

Task Type	Dataset	#nodes	#edges	Density	#labels
	CTD DDA	12,765	92,813	0.11%	-
	NDFRT DDA	13,545	56,515	0.06%	-
Link Prediction	DrugBank DDI	2,191	242,027	10.08%	-
	STRING PPI	15,131	359,776	0.31%	-
Node Classification	Clin Term COOC	48,651	1,659,249	0.14%	31

3. Code

The graph embedding learning for Laplician Eigenmap, Graph Factorization, HOPE, GraRep, DeepWalk, node2vec, LINE, SDNE uses the code from OpenNE The code of struc2vec and GAE is from their authors. To ensure different source code could run successfully in our framework, we modify part of their source code.

Requirements:

pip install -r requirements.txt

Python==3.6
numpy==1.14.0
networkx==2.0
scipy==0.19.1
tensorflow==1.10.0
gensim==3.0.1
scikit-learn==0.19.0
tqdm==4.28.1

General Options

--input, input graph file. Only accepted edgelist format.
--output, output graph embedding file.
--task, choose to evaluate the embedding quality based on a specific prediction task (i.e., link-prediction, node-classification, none (no eval), default is none)
--testing-ratio, testing set ratio for prediction tasks. Only applied when --task is not none. The default is 0.2
--dimensions, the dimensions of embedding for each node. The default is 100.
--method, the name of embedding method
--label-file, the label file for node classification.
--weighted, true if the input graph is weighted. The default is False.
--eval-result-file, the filename of eval result (save the evaluation result into a file). Skip it if there is no need.

Specific Options

Matrix Factorization-based methods:
- --kstep, k-step transition probability matrix for GraRep. The default is 4. It must divide the --dimension.
- --weight-decay, coefficient for L2 regularization for Graph Factorization. The default is 5e-4.
- --lr, learning rate for gradient descent in Graph Factorization. The default is 0.01.
Random Walk-based methods:
- --number-walks, the number of random walks to start at each node.
- --walk-length, the length of the random walk started at each node.
- --window-size, window size of node sequence.
- --p, --q, two parameters that control how fast the walk explores and leaves the neighborhood of starting node. The default values of p, q are 1.0.
- --OPT1, --OPT2, --OPT3, three running time efficiency optimization strategies for struc2vec. The default values are True.
- --until-layer, calculation until the layer. A hyper-parameter for struc2vec. The default is 6.
Neural Network-based methods:
- --lr, learning rate for gradient descent. The default is 0.01.
- --epochs, training epochs. The default is 5. Suggest to set a small value for LINE and SDNE (e.g., 5), and a large value for GAE (e.g., 500).
- --bs, batch size. Only applied for SDNE. The default is 200.
- --negative-ratio, the negative sampling ratio for LINE. The default is 5.
- --order, the order of LINE, 1 means first order, 2 means second order, 3 means first order + second order. The default is 2.
- --alpha, a hyperparameter in SDNE that balances the weight of 1st-order and 2nd-order proximities. The default is 0.3.
- --beta', a hyperparameter in SDNE that controls the reconstruction weight of the nonzero elementsin the training graph. The default is 0.
- --dropout, dropout rate. Only applied for GAE. The default is 0.
- --hidden, number of units in hidden layer. Only applied for GAE. The default is 32.
- --gae_model_selection, GAE model variants: gcn_ae or gcn_vae. The default is gcn_ae.

Running example

python src/main.py --input ./data/DrugBank_DDI/DrugBank_DDI.edgelist --output ./embeddings/DeepWalk_DrugBank_DDI.txt --method DeepWalk --task link-prediction --eval-result-file eval_result.txt

python src/main.py --input ./data/Clin_Term_COOC/Clin_Term_COOC.edgelist --label-file ./data/Clin_Term_COOC/Clin_Term_COOC_labels.txt --output ./embeddings/LINE_COOC.txt --method LINE --task node-classification  --weighted True```

4. Citation

Since the paper is under review, please kindly cite the repo directly if you use the code or the datasets in this repo:

@misc{BioNEV,
 author = {Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, M. Simon and Zhang, Wen and Zhang, Ping and Sun, Huan},
 title = {BioNEV: Biomedical Network Embedding Evaluation},
 url = {https://github.com/xiangyue9607/BioNEV},
}

ddomingof/BioNEV