https://github.com/annamalai-nr/subgraph2vec_tf

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper
https://github.com/annamalai-nr/subgraph2vec_tf

deep-learning deep-neural-networks graph-kernels kdd kernel-methods skipgram tensorflow

Last synced: about 1 month ago
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This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

• Host: GitHub
• URL: https://github.com/annamalai-nr/subgraph2vec_tf
• Owner: annamalai-nr
• Created: 2017-10-06T18:18:56.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-10-13T05:10:51.000Z (about 7 years ago)
• Last Synced: 2024-10-23T13:35:39.470Z (about 2 months ago)
• Topics: deep-learning, deep-neural-networks, graph-kernels, kdd, kernel-methods, skipgram, tensorflow
• Language: Python
• Homepage: https://sites.google.com/site/subgraph2vec/
• Size: 2.63 MB
• Stars: 26
• Watchers: 3
• Forks: 11
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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• awesome-graph-classification - [Python Reference

README

        
# subgraph2vec

This repository contains the "tensorflow" implementation of our paper "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs". 

The paper could be found at: https://arxiv.org/pdf/1606.08928.pdf 

#### Dependencies

This code is developed in python 2.7. It is ran and tested on Ubuntu 14.04 and 16.04.

It uses the following python packages:

1. tensorflow (version == 0.12.1)

2. networkx (version <= 1.11)

3. joblib (version <= 0.11)

4. scikit-learn (+scipy, +numpy)

#####  The procedure for setting up subgraph2vec is as follows:

	1. git clone the repository (command: git clone https://github.com/MLDroid/subgraph2vec_tf.git )

	2. untar the data.tar.gz tarball

#####  The procedure for obtaining rooted subgraph vectors using subgraph2vec and performing graph classification is as follows:

	1. move to the folder "src" (command: cd src) (also make sure that kdd 2015 paper's (Deep Graph Kernels) datasets are available in '../data/kdd_datasets/dir_graphs/')

	2. run main.py --corpus  --class_labels_file_name  file to:

		*Generate the weisfeiler-lehman kernel's rooted subgraphs from all the graphs 

		*Train skipgram model to learn subgraph embeddings. The same will be dumped in ../embeddings/ folder

		*Perform graph classification using graph kernel and deep graph kernel

	3. example: 

		*python main.py --corpus ../data/kdd_datasets/mutag --class_labels_file_name ../data/kdd_datasets/mutag.Labels 

		*python main.py --corpus ../data/kdd_datasets/proteins --class_labels_file_name ../data/kdd_datasets/proteins.Labels --batch_size 16 --embedding_size 128 --num_negsample 5

	

#### Other command line args:

	optional arguments:

		-h, --help            show this help message and exit

		-c CORPUS, --corpus CORPUS

				        Path to directory containing graph files to be used

				        for graph classification or clustering

		-l CLASS_LABELS_FILE_NAME, --class_labels_file_name CLASS_LABELS_FILE_NAME

				        File name containg the name of the sample and the

				        class labels

		-o OUTPUT_DIR, --output_dir OUTPUT_DIR

				        Path to directory for storing output embeddings

		-b BATCH_SIZE, --batch_size BATCH_SIZE

				        Number of samples per training batch

		-e EPOCHS, --epochs EPOCHS

				        Number of iterations the whole dataset of graphs is

				        traversed

		-d EMBEDDING_SIZE, --embedding_size EMBEDDING_SIZE

				        Intended subgraph embedding size to be learnt

		-neg NUM_NEGSAMPLE, --num_negsample NUM_NEGSAMPLE

				        Number of negative samples to be used for training

		-lr LEARNING_RATE, --learning_rate LEARNING_RATE

				        Learning rate to optimize the loss function

		--n_cpus N_CPUS       Maximum no. of cpu cores to be used for WL kernel

				        feature extraction from graphs

		--wlk_h WLK_H         Height of WL kernel (i.e., degree of rooted subgraph

				        features to be considered for representation learning)

		-lf LABEL_FILED_NAME, --label_filed_name LABEL_FILED_NAME

				        Label field to be used for coloring nodes in graphs

				        using WL kenrel

		-v VALID_SIZE, --valid_size VALID_SIZE

				        Number of samples to validate training process from

				        time to time

## Contact ##

In case of queries, please email: [email protected] OR [email protected]

#### Reference 

	Please consider citing the follow paper when you use this code.

	@article{narayanansubgraph2vec,

	  title={subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs},

	  author={Narayanan, Annamalai and Chandramohan, Mahinthan and Chen, Lihui and Liu, Yang and Saminathan, Santhoshkumar}

	}

## Acknowledgements ##

Thanks to Zhang Xinyi (https://github.com/XinyiZ001) for the support on coding/testing subgraph2vec TF version.